Method for autonomously scanning and constructing a representation of a stand of trees

ABSTRACT

One variation of a method includes: accessing a boundary of a stand of trees; defining an array of scan zones within the boundary; accessing a first sequence of images representing treetops in a first scan zone; accessing a second sequence of images representing bases of trees in the first scan zone; accessing a third sequence of images representing bases of trees in a second scan zone; accessing a fourth sequence of images representing treetops in the second scan zone; interpolating canopy characteristics of trees between the first scan zone and the second scan zone based on the first and fourth sequences of images; interpolating lower tree characteristics of trees between the first scan zone and the second scan zone based on the second and third sequences of images; and compiling canopy and lower tree characteristics into a virtual representation of tree characteristics across the stand of trees.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/343,979, filed on 19 May 2022, which is incorporated in its entiretyby this reference.

TECHNICAL FIELD

This invention relates generally to the field of forestry and morespecifically to a new and useful method for measuring tree populationand health in the field of forestry.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A and 1B are flowchart representations of a method;

FIGS. 2A and 2B are flowchart representations of one variation of themethod; and

FIGS. 3A-3B are schematic representations of one variation of themethod.

DESCRIPTION OF THE EMBODIMENTS

The following description of embodiments of the invention is notintended to limit the invention to these embodiments but rather toenable a person skilled in the art to make and use this invention.Variations, configurations, implementations, example implementations,and examples described herein are optional and are not exclusive to thevariations, configurations, implementations, example implementations,and examples they describe. The invention described herein can includeany and all permutations of these variations, configurations,implementations, example implementations, and examples.

1. Method

As shown in FIGS. 1A, 1B, 2A, 2B, 3A, and 3B, a method S100 forautonomously scanning and constructing a representation of a stand oftrees includes: accessing a boundary of a stand of trees in Block S110;and defining an array of scan zones within the boundary of the stand oftrees in Block S120. The method S100 further includes, defining a flightpath in Block S130 including: a first waypoint at a first altitude abovea first set of trees within a first scan zone; a second waypoint at asecond altitude proximal a first floor within the first scan zone; athird waypoint at a third altitude proximal a second floor within asecond scan zone; a fourth waypoint at a fourth altitude above a secondset of trees within the second scan zone; and a fifth waypoint at afifth altitude above a third set of trees within a third scan zone.

The method S100 also includes, accessing a first set of images in BlockS140 including: a first sequence of images representing tops of thefirst set of trees and captured by an aerial vehicle proximal the firstwaypoint; a second sequence of images representing bases of the firstset of trees and captured by the aerial vehicle proximal the secondwaypoint; a third sequence of images representing bases of the secondset of trees and captured by the aerial vehicle proximal the thirdwaypoint; a fourth sequence of images representing tops of the secondset of trees and captured by the aerial vehicle proximal the fourthwaypoint; and a fifth sequence of images representing tops of the thirdset of trees and captured by the aerial vehicle proximal the fifthwaypoint.

The method S100 further includes: interpolating a first set of treecanopy characteristics of a fourth set of trees between the first scanzone and the second scan zone based on visual features detected in thefirst sequence of images and the fourth sequence of images in BlockS150; interpolating a first set of lower tree characteristics of thefourth set of trees between the first scan zone and the second scan zonebased on visual features detected in the second sequence of images andthe third sequence of images in Block S152; interpolating a second setof tree canopy characteristics of a fifth set of trees between thesecond scan zone and the third scan zone based on visual featuresdetected in the fourth sequence of images and the fifth sequence ofimages in Block S154; and compiling the first set of tree canopycharacteristics, the first set of lower tree characteristics, and thesecond set of tree canopy characteristics into a virtual representationof tree characteristics across the stand of trees in Block S160

1.1 Variation: Aerial Vehicle Traversal+Three-Dimensional Representation

One variation of the method S100 includes: accessing an overhead imagedepicting a stand of trees in Block S112; overlaying a boundary of thestand of trees onto the overhead image in Block S110; projecting anarray of scan zones onto the overhead image within the boundary of thestand of trees in Block S120; and deploying an aerial vehicle to executea flight path through the array of scan zones in Block S130. Thisvariation of the method S100 further includes, during the flight path:traversing the aerial vehicle across the stand of trees to a firstwaypoint above a first scan zone in Block S132; vertically traversingthe aerial vehicle to a second waypoint proximal a first floor of thefirst scan zone in Block S134; laterally traversing the aerial vehiclefrom the first scan zone to a third waypoint proximal to a second floorof a second scan zone in Block S136; and vertically traversing theaerial vehicle to a fourth waypoint above the second scan zone in BlockS138.

This variation of the method S100 also includes accessing a set ofimages captured by the aerial vehicle in Block S140 including: a firstsequence of images representing tops of a first set of trees within thefirst scan zone; a first intermediate vertical sequence of imagesrepresenting trunks of the first set of trees; a second sequence ofimages representing bases of the first set of trees within the firstscan zone; a third sequence of images representing bases of a second setof trees within the second scan zone; a second intermediate verticalsequence of images representing trunks of the second set of trees; and afourth sequence of images representing tops of the second set of treeswithin the second scan zone.

This variation of the method S100 further includes: compiling the firstsequence of images, the first intermediate vertical sequence of images,and the second sequence of images into a first three-dimensionalrepresentation of the first scan zone in Block S162; compiling the thirdsequence of images, the second intermediate vertical sequence of images,and the fourth sequence of images into a second three-dimensionalrepresentation of the second scan zone in Block S164; and assembling thefirst three-dimensional representation of the first scan zone and thesecond three-dimensional representation of the second scan zone into athird three-dimensional representation of the stand of trees in BlockS166.

1.2 Variation: Representation of Tree Characteristics

One variation of the method S100 includes: accessing a boundary of astand of trees in Block S110; defining an array of scan zones within theboundary of the stand of trees in Block S120; and defining a flight pathincluding a set of waypoints representing positions within the array ofscan zones in Block S130.

This variation of the method S100 further includes accessing a set ofimages captured by the aerial vehicle in Block S140 including: a firstsequence of images representing tops of a first set of trees within afirst scan zone; a second sequence of images representing bases of thefirst set of trees within the first scan zone; a third sequence ofimages representing bases of a second set of trees within a second scanzone; a fourth sequence of images representing tops of the second set oftrees; and a fifth sequence of images representing tops of a third setof trees within a third scan zone.

This variation of the method S100 also includes: interpolating a firstset of tree canopy characteristics of a fourth set of trees between thefirst scan zone and the second scan zone based on visual featuresdetected in the first sequence of images and the fourth sequence ofimages in Block S150; interpolating a first set of lower treecharacteristics of the fourth set of trees between the first scan zoneand the second scan zone based on visual features detected in the secondsequence of images and the third sequence of images in Block S152;interpolating a second set of tree canopy characteristics of a fifth setof trees between the second scan zone and the third scan zone based onvisual features detected in the fourth sequence of images and the fifthsequence of images in Block S154; and compiling the first set of treecanopy characteristics, the first set of lower tree characteristics, andthe second set of tree canopy characteristics into a virtualrepresentation of tree characteristics across the stand of trees inBlock S160.

1.3 Variation: Audit Tool

One variation of the method S100 includes: accessing an overhead imagedepicting a stand of trees in Block S112; accessing a set of groundimages representing bases of the stand of trees and captured by anaerial vehicle in Block S140; and isolating a first set of trees, in thestand of trees, depicted in the set of ground images.

This variation of the method S100 further includes, for each tree in thefirst set of trees: detecting a first region of the set of ground imagesdepicting the tree in Block S142; extracting a first set of visualfeatures from the first region of the set of ground images in BlockS144; characterizing a value of a metric of the tree based on the firstset of visual features in Block S146; detecting a second region of theoverhead image depicting the tree in Block S142; extracting a second setof visual features from the second region of the overhead image in BlockS144; and storing the value of the metric and the second set of visualfeatures in a container in a set of containers in Block S148.

This variation of the method S100 also includes generating a metricfunction representing a correlation between values of the metric andvisual features within the stand of trees in Block S156 and based on theset of containers, isolating a second set of trees, in the stand oftrees, excluded from the set of ground images and depicted in theoverhead image and, for each tree in the second set of trees: detectinga third region of the overhead image depicting the tree in Block S142;extracting a third set of visual features from the third region of theoverhead image in Block S144; and characterizing a second value of themetric of the tree based on the third set of visual features in BlockS146. This variation of the method S100 further includes compilingvalues of the metric, for the first set of trees and the second set oftrees, into a composite value of the metric for the stand of trees inBlock S170.

1.4 Variation: Confidence Scores+Virtual Representation

As shown in FIG. 2A, one variation of the method S100 includes:accessing an overhead image depicting a stand of trees in Block S112;accessing a set of ground images representing bases of the stand oftrees and captured by an aerial vehicle in Block S140; and isolating afirst set of trees, in the stand of trees, depicted in the set of groundimages.

This variation of the method S100 further includes, for each tree in thefirst set of trees: detecting a first region of the set of ground imagesdepicting the tree in Block S142; extracting a first set of visualfeatures from the first region of the set of ground images in BlockS144; characterizing a first value of a metric of the tree based on thefirst set of visual features in Block S146; detecting a second region ofthe overhead image depicting the tree in Block S142; extracting a secondset of visual features from the second region of the overhead image inBlock S144; calculating a confidence score of the first value of themetric of the tree proportional to a resolution of the first region ofthe set of ground images depicting the tree in Block S146; and storingthe confidence score, the first value of the metric, and the second setof visual features in a container in a set of containers in Block S148.

This variation of the method S100 also includes isolating a second setof trees, in the stand of trees, excluded from the set of ground imagesand, for each tree in the second set of trees: detecting a third regionof the overhead image depicting the tree in Block S142; extracting athird set of visual features from the third region of the overhead imagein Block S144; and characterizing a second value of the metric of thetree based on the third set of visual features in Block S146. Thisvariation of the method S100 further includes: generating a virtualrepresentation of confidence scores for the first set of trees in BlockS168; and compiling values of the metric, for the first set of trees andthe second set of trees, into a composite value of the metric for thestand of trees annotated with the virtual representation of confidencescores in Block S170.

1.5 Variation: Metric Function

One variation of the method S100 includes: accessing a set of overheadimages depicting a stand of trees in Block S112; accessing a set ofground images representing bases of the stand of trees in Block S140;and isolating a first set of trees, in the stand of trees, depicted inthe set of ground images.

This variation of the method S100 further includes, for each tree in thefirst set of trees: identifying a first image, in the set of groundimages, depicting the tree in Block S142; extracting a first set ofvisual features from the first image in Block S144; characterizing avalue of a metric of the tree based on the first set of visual featuresin Block S146; detecting a first overhead image, in the set of overheadimages, depicting the tree in Block S142; extracting a second set ofvisual features from the first overhead image in Block S144; and storingthe value of the metric and the second set of visual features in acontainer in a set of containers in Block S148.

This variation of the method S100 also includes generating a metricfunction representing a correlation between values of the metric andvisual features within the stand of trees in Block S156 and based on theset of containers, isolating a second set of trees, in the stand oftrees, excluded from the set of ground images, and for each tree in thesecond set of trees: detecting a second image, in the set of overheadimages, depicting the tree in Block S142; extracting a third set ofvisual features from the second overhead image in Block S144; andcharacterizing a second value of the metric of the tree based on thethird set of visual features and the metric function in Block S146. Thisvariation of the method S100 further includes compiling values of themetric, for the first set of trees and the second set of trees, into acomposite value of the metric for the stand of trees in Block S170.

2. Applications

Generally, Blocks of the method S100 can be executed by a computersystem in conjunction with an aerial vehicle to: access an overheadimage of a stand of trees (e.g., an aerial image, satellite image, ageoreferenced map); define an array of georeferenced scan zones within aboundary of the stand of trees; define a flight path through the arrayof scan zones for execution by the aerial vehicle; collect images oftrees, images of the forest floor, and ambient data within these scanzones; implement machine learning and other computer vision techniques(e.g., object detection, edge detection, template matching) to detectvisual features and extract characteristics (e.g., tree height, treebase diameter, bark characteristics, canopy characteristics) of treeswithin these scan zones from these images; and construct a virtualrepresentation (e.g., three-dimensional representation) linking ambientconditions, tree characteristics, and images of the stand of trees.

More specifically, the computer system can access a low-resolutionoverhead image (e.g., aerial image, satellite image) of the stand oftrees and virtually overlay a low-density array of discrete, isolatedscan zones onto the overhead image, such as a two-dimensional grid arrayof 25-meter-diameter scan zones at 200-meter lateral and longitudinalpitch distances. The computer system can then define georeferencedcoordinates for these scan zones and generate a flight path executableby the aerial vehicle to capture optical data (e.g., high-resolutionimages, depth images) and non-optical data (e.g., ambient data) of thestand of trees. The aerial vehicle can then navigate along the flightpath to traverse above the tops of trees in each scan zone; verticallytraverse between trees in each scan zone; traverse below the canopy oftrees in each scan zone; and alternate above-canopy and below-canopytraversal between adjacent scan zones in the stand of trees, as shown inFIG. 3B.

The computer system can leverage the virtual representation of the standof trees to predict tree characteristics (e.g., height, carbon volume,health, pest presence) and/or ambient conditions in regions of the standof trees—unscanned by the aerial vehicle and/or excluded from the arrayof scan zones—based on visual features detected in the overhead image ofthe stand of trees.

Furthermore, the computer system can implement computer visiontechniques (e.g., object detection, edge detection, template matching)to detect visual features (e.g., bark characteristics, pixel width,foliage characteristics) of each tree depicted in a high-resolutionground image and characterize a value of a metric associated with thesevisual features such as: tree type; tree species; pest risk; basediameter; fire risk; defects; carbon capture; etc. The computer systemcan then generate a metric function (e.g., a linear regression)representing a correlation between values of this metric and thesevisual features and leverage the metric function to characterize a valueof the metric of each tree depicted in a low-resolution overhead imageof the stand of trees. The computer system can further calculate aconfidence score of each value of the metric proportional to theresolution of the high-resolution ground image depicting each tree.Additionally or alternatively, the computer system can leverage visualfeatures and the linear regression to generate a confidence function tocalculate confidence scores of trees depicted in the low-resolutionoverhead image.

2.1 Aerial Vehicle

The computer system can upload the flight path to an aerial vehicle,such as an autonomous unmanned aerial vehicle including a chassis,equipped with a flight controller, a geolocation module, acommunications module, and a suite of sensors configured to capturephotographic images of trees, capture depth maps, capture thermographicimages, and/or collect ambient (e.g., environmental) data.

The aerial vehicle can include a suite of optical sensors mounted to thechassis via a set of gimbals. The suite of sensors includes: an RGBcamera, a multispectral camera, and/or a LIDAR sensor facing downwardlyfrom the chassis and configured to capture optical data of trees as theaerial vehicle traverses above tops of trees and downwardly within ascan zone. The aerial vehicle can also include a similar second suite ofoptical sensors facing upwardly from the chassis and configured tocapture optical data of trees as the aerial vehicle traverses below thetree canopy and upwardly within a scan zone. The aerial vehicle canmanipulate the first and second suites of optical sensors to capturelateral-, upward-, and downward-facing images during operation.Additionally or alternatively, the aerial vehicle can include a set oflateral-facing optical sensors. The aerial vehicle can further include:a temperature sensor; a humidity sensor; a light level sensor; and anultrasonic proximity sensor.

During operation, the aerial vehicle can: access the flight pathgenerated by the computer system; autonomously navigate along theflightpath; capture optical and ambient data within each scan zone andselectively between scan zones (e.g., along limited, linear paths). Theaerial vehicle can: autonomously navigate around tops of trees in thescan zone and capture optical images via downward-facing opticalsensors; process images in real-time to identify a gap between the treecanopy and a forest floor; autonomously navigate through this gap towardthe forest floor; and then autonomously navigate around bases of treesin the scan zone and capture optical images via upward, lateral, and/ordownward-facing optical sensors. The aerial vehicle can autonomouslynavigate under the tree canopy to a next scan zone within the stand oftrees and capture optical images via upward, lateral, and/ordownward-facing optical sensors. The aerial vehicle can: autonomouslynavigate around bases of trees in this scan zone and capture opticalimages via upward-facing optical sensors; process images in real-time toidentify a gap in the tree canopy between tops of trees in this scanzone; autonomously navigate through this gap to a location above tops ofthese trees; and then autonomously traverse above tops of trees in thescan zone and capture optical images via the set of downward-facingoptical sensors.

The aerial vehicle can also execute simultaneous localization andmapping (SLAM) techniques to autonomously assemble a three-dimensionaldepth and/or color map of trees along the flight path.

2.2 Tree Feature Extraction

The computer system can then: access georeferenced optical and/orambient data collected by the aerial vehicle, such as in real-time orfollowing completion of the flight path; implement computer visiontechniques to detect and extract characteristics of trees (e.g., treebase diameter, tree color, tree height, foliage density, canopycharacteristics, tree type) in scan zones from these images; derivecorrelations between these tree characteristics; and then assemble thesecorrelations into tree characteristics model for the stand of trees.

The computer system can also aggregate optical data (e.g., opticalimages, depth images) collected by the aerial vehicle while navigatingalong the flight path into a three-dimensional representation (e.g., asparse spatial representation) of the stand of trees. The computersystem can further leverage these optical data and the overhead image ofthe stand of trees to interpolate or predict characteristics of trees(e.g., locations, sizes, foliage density, health, canopycharacteristics) excluded from the array of scan zones (e.g., unscannedtrees, trees arranged between scan zones); and populate thethree-dimensional representation of the stand of trees with theseadditional predicted tree characteristics.

2.3 Stand Metrics

Furthermore, the computer system can extrapolate metrics of the stand oftrees from the three-dimensional representation and present thesemetrics to a user via a user interface (e.g., user portal, audit tool).For example, the computer system can present a visual representation ofthe stand of trees and corresponding metrics to the user within the userportal, such as including: gross tree count; tree count by species;average tree height; average tree base diameter; total timber volume;tree density; tree health; pest presence or severity; and/or fire risk.

Additionally, the computer system can present metrics of the stand oftrees as a management report, as a heatmap of base diameters of trees,and/or as a histogram of tree quantities for review by the user withinthe user portal. The user can further interface with the user portal toreview metrics of interest and filter the metrics such as by confidencescore.

3. Aerial Vehicle

In one implementation, the aerial vehicle defines an autonomous airbornerobotic system including: a chassis; a flight controller; a geolocationmodule; a wireless communications module; and a sensor suite includingoptical sensors (e.g., an RGB camera, a hyper-spectral camera, a LIDARsensor), proximity sensors, and/or ambient condition sensors (e.g.,temperature sensor, humidity/moisture sensor, light intensity sensor).The aerial vehicle is configured to: autonomously navigate to above,below, and around a stand of trees; record optical (e.g., RGB, depth,multispectral) images of trees and other objects within the stand oftrees; write a georeferenced location and altitude of the aerial vehicleto each optical image (e.g., via the geolocation module); track andmaintain minimum distances from other objects (e.g., via the LIDARsensor); record ambient conditions (e.g., via ambient sensors); andtransmit image and geospatial location data to the computer system viathe communications module.

In one implementation, the aerial vehicle includes: a suite ofupward-facing optical sensors (e.g., RGB color and LIDAR depth sensors)mounted above the aerial vehicle chassis and oriented with fields ofview directed upwardly from the chassis; and a suite of downward-facingoptical sensors mounted below the aerial vehicle chassis and orientedwith fields of view directed downwardly from the aerial vehicle chassis.

In another implementation, the aerial vehicle includes a suite ofproximity sensors oriented about the aerial vehicle chassis andconfigured to detect proximity of objects—such as tree limbs or biowasteon a forest floor—above, below, and adjacent the aerial vehicle. Forexample, the aerial vehicle can include ultrasonic, electromagnetic,RADAR, and/or SONAR sensors fixedly- or dynamically-mounted to theaerial vehicle chassis.

4. Computer System

The computer system—such as a remote server—can manipulate overheadimages depicting a stand of trees and generate a flight path defining anorder of waypoints for an aerial vehicle to execute. Additionally, thecomputer system can receive optical data (e.g., hi-resolution images)and non-optical data (e.g., ambient data, proximity data, geospatiallocation data) from the aerial vehicle via the communications module.The computer system can also execute simultaneous localization andmapping (or “SLAM”) techniques to autonomously assemble athree-dimensional depth and/or color map of the stand of trees along theflight path of the aerial vehicle based on data collected by the suiteof sensors mounted to the aerial vehicle.

Furthermore, the computer system can then: access these optical andnon-optical data collected by the aerial vehicle, such as in real-timeor following termination of the flight path; implement computer visiontechniques to detect and extract characteristics of a set of trees(e.g., tree base diameter, tree color, foliage density, pest pressure,carbon volume, tree trunk height, lower tree characteristics, canopytree characteristics) in each scan zone from these images; manipulatethese optical and non-optical data to generate a spatial zone model (or“three-dimensional representation”) of each scan zone within a boundaryof the stand of trees; and construct models linking thesecharacteristics of trees within the stand of trees. The computer systemcan then assemble a three-dimensional representation of the stand oftrees and extract metrics and insights of the forest, and/or presentthese metrics and insights to a user (e.g., stand manager or owneraffiliated with the forest) based on these data.

4.1 Pre-Scan Setup

As shown in FIGS. 1A and 1B, in preparation for scanning and modeling astand of trees, the computer system can access an overhead image of thestand of trees, such as an existing satellite image or an aerialscouting image captured by an external manned or unmanned aerialscouting vehicle. The computer system can: virtually overlay a set ofscan zones onto the overhead image; generate a nominal flight pathextending within and between the scan zones; and upload the nominalflight path to the aerial vehicle for execution.

In one implementation, the computer system can: access a low-resolutionoverhead image depicting a stand of trees (e.g., a previously recordedhigher-altitude aerial image, a satellite image); access a boundary ofthe stand of trees; projects the boundary onto the overhead image;virtually overlay an array of scan zones, arranged in a two-dimensionalgrid, onto the overhead image within the boundary of the stand of trees;assign a set of georeferenced coordinates to each sample zone; andcalculate a nominal scan route (or “flight path) from a scan initiationpoint (e.g., at a first scan zone), through each scan zone, and to ascan termination point (e.g., at a last scan zone) for execution by anaerial vehicle.

4.2 Pre-Scan Flyover

In one variation, prior to the initiation of the flight path, thecomputer system can: deploy the aerial vehicle to overfly the stand oftrees and capture a set of georeferenced overhead images of the stand oftrees characterized by an initial resolution (e.g., low-resolutionimages); access the set of images captured by the aerial vehicle; stitchthe set of images into a composite overhead image of the stand of trees;virtually overlay an array of scan zones, onto the composite overheadimage; assign georeferenced coordinates to each scan zone; and calculatea flight path from a scan initiation point (e.g., at a first scan zone),through each scan zone, and to a scan termination point (e.g., at a lastscan zone).

For example, the computer system can deploy a set of aerial vehicles toa stand of trees to capture a set of overhead images of the stand oftrees. In this example, the computer system deploys a first aerialvehicle to collect a set of high-resolution, low altitude overheadimages of the stand of trees within the boundaries of the stand oftrees. The first aerial vehicle transmits this set of images andnon-optical data to the computer system in real-time. The computersystem can then: compile (i.e., stitch) the set of images captured bythe aerial vehicle into a composite overhead image of the stand oftrees; and virtually overlay an array of sample zones, arranged in atwo-dimensional grid, onto the composite overhead image. Then, thecomputer system can: assign a set of discrete geolocation coordinates toeach scan zone in the array of scan zones; calculate a flight path forexecution by a next aerial vehicle in the set of aerial vehicles; andtransmit the flight path to the next aerial vehicle. The computer systemcan then deploy the next aerial vehicle to execute the flight path forthe stand of trees.

Therefore, the computer system can access an overhead image captured bya scouting aerial vehicle at an altitude above the tops of the stand oftrees and/or deploy an aerial vehicle to capture a set ofhigh-resolution images at a lower altitude (e.g., near the tops of thestand of trees) and stitch these images into a high(er)-resolutionoverhead image of the stand of trees.

4.3 Grid Array of Scan Zones

In one variation, the computer system can: access an overhead imagedepicting the stand of trees; extract visual features from regions ofthe overhead image; characterize a difference between visual features ofa first region and a second region of the overhead image; and project anarray of scan zones onto the overhead image—within the boundary of thestand of trees—based on the difference.

Furthermore, the computer system can virtually overlay a two-dimensionalgrid array of 25-meter-diameter scan zones at 200-meter lateral andlongitudinal pitch distances onto the overhead image. In this variation,the computer system can project a two-dimensional grid array of scanzones onto the overhead image, each scan zone: defining a minimumdiameter within a target diameter range (e.g., between 20-meter-diameterand 30-meter-diameter, between 23-meter-diameter and 27-meter-diameter);defining a lateral pitch distance greater than the minimum diameter andless than a maximum width of the boundary of the stand of trees (e.g.,200 meter lateral pitch distance); and defining a longitudinal pitchdistance greater than the minimum diameter and less than a maximumlength of the boundary of the stand of trees (e.g., 200 meterlongitudinal pitch distance).

For example, the computer system can: access an overhead image depictingthe stand of trees; extract a first set of visual features from a firstregion—representing tree characteristics of a first set of trees—of theoverhead image; extract a second set of visual features from a secondregion—representing tree characteristics of a second set of trees—of theoverhead image; and characterize a difference between the first set ofvisual features and the second set of visual features. Then, in responseto the difference between the first set of visual features and thesecond set of visual features exceeding a difference threshold (e.g.,70%), the computer system can: project a first scan zone (e.g., defininga 25-meter-diameter) onto the first region of the overhead image toencompass the first set of trees; and project a second scan zone (e.g.,defining a 25-meter-diameter, a 200 meter lateral pitch distance, and a200 meter longitudinal pitch distance from the first scan zone), ontothe second region of the overhead image to encompass the second set oftrees.

The computer system can repeat these methods and techniques for eachother set of trees and for each other scan zone to virtually overlay thearray of scan zones onto the overhead image. However, the computersystem can overlay the array of scan zones onto the overhead image inany other way.

4.4 Flight Path

The computer system can then define georeferenced coordinates for thesescan zones and generate a georeferenced flight path executable by anaerial vehicle to capture a sparse set of images and ambient data of thestand of trees. Furthermore, the computer system can define a flightpath from a scan initiation point (e.g., at a first scan zone), througheach scan zone, and to a scan termination point (e.g., at a last scanzone) for execution by the aerial vehicle.

In one implementation, the computer system can define a flight pathincluding: a first waypoint at a first altitude above a first set oftrees within a first scan zone; a second waypoint at a second altitudeproximal a first floor (e.g., within a threshold distance of the firstfloor) within the first scan zone; a third waypoint at a third altitudeproximal a second floor (e.g., within a threshold distance of the secondfloor) within a second scan zone; a fourth waypoint at a fourth altitudeabove a second set of trees within the second scan zone; and a fifthwaypoint at a fifth altitude above a third set of trees within a thirdscan zone. The computer system can then upload the flight path to anaerial vehicle to scan the stand of trees, as further described below.

4.5 Gap Preplanning

In one implementation, the computer system can access an overhead imageof the stand of trees prior to deploying the aerial vehicle, andimplement computer vision techniques (e.g., edge detection, objectdetection, spectral analysis, depth perception) to: identifydiscolorations and/or abnormalities in the overhead image of the canopythat may correspond to gaps in the canopy for traversal by the aerialvehicle during the scan routine; assign georeferenced coordinates to theidentified gap; and modify the flight path to intersect the identifiedgap.

In particular, prior to deploying the aerial vehicle, the computersystem can: access an overhead image of the stand of trees; isolate aregion of the image corresponding to a first scan zone; and isolatepixels representing darker colors (e.g., lower color intensity) withinthe region of the image. The computer system can then: identify clustersof pixels representing these darker colors within other regions of theoverhead image; characterize a minimum width of each identified clusterof pixels from each region of the overhead image; discard clusters ofpixels characterized by a minimum width less than the maximum width ofthe aerial vehicle; and record the remaining cluster of pixels aspotential gaps in the canopy of the stand of trees. The computer systemcan access the geolocation coordinates associated with each potentialgap in the canopy; adjust the flight path to intersect the coordinatesof each potential gap within the first scan zone.

Therefore, the computer system can enable the aerial vehicle to detectgaps in the canopy with a minimum width greater than a maximum dimensionof the aerial vehicle and traverse vertically from above-canopy tobelow-canopy of the stand of trees—and vice versa.

5. First Scan Zone

The computer system can deploy the aerial vehicle to the first scan zoneto collect optical and non-optical data (e.g., ambient condition data)of the first scan zone. The aerial vehicle can then collect optical dataand location data of a first set of trees within the first scan zone viathe optical sensor and geolocation module.

In one implementation, the aerial vehicle can traverse above a first setof trees within the first scan zone and capture images above the canopyof the first set of trees proximal the first waypoint. The aerialvehicle can then: analyze these images to identify a gap in the canopy.The aerial vehicle can then execute a downward vertical traversalthrough the gap in the canopy into the first scan zone and capture asequence of images and ambient data during the traversal. The aerialvehicle can further detect a proximity to the forest floor via the depthsensor and execute a base scan routine through the first scan zone tocollect optical and ambient condition data within the first scan zone.At the conclusion of the base scan routine, the aerial vehicle cantraverse underneath the canopy to a second scan zone.

5.1 Over-Canopy Transversal to First Scan Zone

In one implementation, the computer system can deploy the aerial vehiclefrom a location proximal the stand of trees. The computer system canthen communicate with a geolocation service, via a GPS module of theaerial vehicle, to retrieve the current geolocation of the aerialvehicle. The computer system can then calculate a flight path from thecurrent location, over the top of the canopy of the stand of trees, to afirst waypoint above the first scan zone.

During traversal to the first scan zone, the aerial vehicle can: monitora current position via the depth sensor and remain on the flight path;and capture a set of georeferenced high resolution overhead images ofthe canopy, via the optical sensor, proximal the flight path. The aerialvehicle can cease traversal in response to detecting a current locationof the aerial vehicle corresponding to the first waypoint (e.g., a setof geolocating coordinates) assigned to the first scan zone.

In one variation, the aerial vehicle can: capture a current image of thecanopy proximal the aerial vehicle; access a previously captured imageof the first scan zone; and apply computer vision techniques (e.g.,template matching, object detection, edge detection) to compare thecurrent image to the previous image. In response to features detected inthe current image corresponding to features detected in the previousimage, the aerial vehicle can initiate the scan routine above the firstscan zone.

5.2 Overhead Canopy Gap Scan

In one implementation, the aerial vehicle can navigate along the flightpath to the first waypoint above the first scan zone and initiate anoverhead gap scanning procedure above the canopy of the first scan zone.The aerial vehicle can: navigate to the first waypoint of the flightpath (e.g., a center point) of the first scan zone; and traverse alongan outward spiral flight path from the center point of the scan zonetoward the perimeter of the scan zone. During the traversal, the aerialvehicle can: collect geolocation data; capture color images of thecanopy of the first scan zone (e.g., tops of trees) proximal the firstwaypoint via an optical sensor facing tops of the stand of trees;collect depth data via a depth sensor (i.e., LIDAR); and collect ambientdata, such as temperature and/or humidity.

5.3 Overhead Canopy Gap Detection

Generally, during the overhead canopy gap scanning procedure, the aerialvehicle can analyze each image and associated depth data in real-timeusing computer vision techniques to identify a gap in the canopy greaterthan a maximum dimension of the aerial vehicle (e.g., maximum width).The aerial vehicle can simultaneously assemble the set ofimages—captured in real-time—into a composite image and aggregate thelocation and depth data associated with each image into a canopy profile(i.e., a spatial representation of the canopy.)

In one implementation, the aerial vehicle can: access the depth images;sequentially rank the depth images by location in the scan zone fromgreatest to least; access an image associated with the greatest depthimage; and analyze the image via computer vision techniques to identifya potential gap larger than the maximum dimension of the aerial vehicleat this location. In one variation, the aerial vehicle can rank thepotential gaps in order of distance from the current location, to reducethe traversal time from the current location to the potential gap in thecanopy, therefore reducing total scan time.

In another implementation, the aerial vehicle can: access a rawtwo-dimensional image depicting the canopy from above (or a compositetwo-dimensional image stitched together from a set of images captured bythe aerial vehicle); identify a set of low color intensity value (i.e.,dark) pixels in the image corresponding to the forest floor (e.g.,filters the image to identify all pixels within the image with a totalcolor intensity value of R, G, and/or B that correspond to dark green orbrown colors associated with earth, or forest floor terrain); identify acluster of low color intensity value pixels within the image; isolate aset of pixels in the cluster with greatest minimum width; access thelocation of the set of pixels with the greatest minimum width, based ongeolocation data associated with the raw two-dimensional image; andidentify the location as a gap in the canopy.

Furthermore, the computer system can assemble (e.g., stitch) depthimages captured by the aerial vehicle into a spatial representationextending below the canopy. The computer system can identify a set ofgreatest depth images (e.g., by rank ordering depth images, byidentifying depth images greater than a threshold depth.) The computersystem can then plane fit the set of greatest depth images to pointsrepresenting a floor in the spatial representation of the scan zone.

Therefore, the aerial vehicle can leverage onboard optical sensors toautonomously scan the canopy for gaps approximating a maximum dimensionof the aerial vehicle. The aerial vehicle can autonomously identify gapsin and navigate through the canopy to capture images of trees below thecanopy. The aerial vehicle can identify and traverse gaps in the canopyto traverse over the canopy and descend into a scan zone through thecanopy to complete an under-canopy scan of the scan zone, and therebynavigate an accurate and repeatable flight path.

5.4 Downward Traversal

In one implementation, the aerial vehicle can navigate from the firstwaypoint above a first set of trees within the first scan zone (e.g.,above the canopy) to face a gap in the canopy. The aerial vehicle thenvertically traverses through the gap and descends downward to a secondwaypoint proximal the floor of the first scan zone. During descent, theaerial vehicle can: capture images of tree trunks and other objectsproximal the aerial vehicle via the optical sensor; continuously detectthe distance between the base of the aerial vehicle and the forest floorvia a distance sensor (e.g., LIDAR) facing the forest floor; and, inresponse to the distance between the base of the aerial vehicle and theforest floor dropping below a threshold distance, cease descent throughthe gap.

In one variation, an ultrasonic proximity sensor is mounted to theaerial vehicle and configured to output signals corresponding todistances between the aerial vehicle and the forest floor. The aerialvehicle can interpret a distance based on a first signal from theproximity sensor and, in response to the distance exceeding a thresholdcollision distance, cease descent and thereby avoid collision with theforest floor.

For example, the aerial vehicle can traverse downward and capture imagesof a set of proximal trees that define the gap boundary in the canopy.Generally, the aerial vehicle can identify the set of proximal trees toinclude a minimum amount of tree trunks (e.g., three) to define thelength and width of the gap boundary. The aerial vehicle can also detecta distance between the aerial vehicle and each tree in the set ofproximal trees. Additionally, the aerial vehicle can capture a verticalsequence of images representing trunks and/or visible branches of anadditional set of trees—located beyond the set of proximal trees andwithin the field of view of the lateral optical sensor—while navigatingfrom the first waypoint to a second waypoint of the first scan zone.Further, the aerial vehicle can capture images of bases of trees (e.g.,underside of the canopy), via the upward-facing optical sensor, andcaptures images of the forest floor, via the downward-facing opticalsensor. The aerial vehicle can then traverse downward and detect adistance from the base of the aerial vehicle to the forest floor via thedistance sensor (e.g., LIDAR sensor.) Then, in response to the distancefalling below a threshold distance (e.g., two feet, one meter), theaerial vehicle can cease the downward traversal.

Therefore, the aerial vehicle can autonomously traverse down through aset of trees to an area below the canopy of the scan zone to reach theforest floor and leverage a distance sensor to avoid collisions withobstacles (e.g., trees, shrubs, branches) within the scan zone.Additionally, the aerial vehicle can calculate a height of each treeproximal the aerial vehicle based on the images captured and thedistance detected from the aerial vehicle to the forest floor.

5.5 Base Scan within First Scan Zone

In one implementation, the aerial vehicle can navigate from the firstwaypoint above the first scan zone to the second waypoint proximal afloor of the first scan zone. The aerial vehicle can then: execute abase scan routine through the first scan zone; and execute simultaneouslocalization and mapping (hereinafter “SLAM”) techniques to avoidcollisions with detected obstacles via the distance sensor. The aerialvehicle can navigate along the flight path and capture: upward imagesdepicting bases of trees (e.g., the underside of the canopy), via anupward-facing optical sensor; downward images depicting the forestfloor, via a downward-facing optical sensor; and lateral imagesdepicting the trunks and branches of trees. Additionally, the aerialvehicle can capture color and depth images of tree canopies, tree bases,and the forest floor via the upward, lateral, and downward-facingsensors. At the conclusion of the base scan routine, the aerial vehiclecan navigate along the flight path to a second scan zone and executeSLAM techniques to avoid obstacles as the aerial vehicle traverses tothe second scan zone.

The aerial vehicle can additionally capture depth images of theunderside of the canopy. The computer system can then access theseunder-canopy depth images to augment the spatial representation of thecanopy. Further, the aerial vehicle can capture ambient conditions(i.e., temperature, humidity, light levels) within the under-canopy areaof the scan zone.

For example, upon completion of the vertical traversal, the aerialvehicle can execute SLAM techniques to navigate along the nominal flightpath and avoid detected obstacles. The aerial vehicle can then captureimages of the underside of the canopy depicting branches, and foliage,and gaps in the canopy via the optical sensor(s) and detect distances totrees or other objects proximal the aerial vehicle via the distancesensor(s) while navigating along the flight path. The aerial vehicle cangradually increase altitude as the aerial vehicle laterally traversesthrough the first scan zone (i.e., completes an upward spiral patternthrough the first scan zone). Upon completion of a segment of the flightpath through the first scan zone, the aerial vehicle can: navigate alongthe flight path to a second scan zone; decreases altitude; and executeSLAM techniques to navigate under the canopy along the nominal flightpath to the second scan zone, as shown in FIG. 3B.

Further, the computer system can compile the georeferenced base scandata captured in the first scan zone with the overhead canopy scan dataof the first scan zone to link base diameters of trees to correspondingtops of trees within the first scan zone by matching the location ofeach tree base identified in the base scan to the location of eachtreetop identified in the canopy scan. In particular, the computersystem can directly detect the height of the trees proximal the aerialvehicle during the vertical traversal. The computer system can furtherapply the detected base-to-height correlation to the remaining treetopsidentified in the canopy of the first scan zone to extrapolate theheight of the trees in the first scan zone.

5.6 Under-Canopy Zone-to-Zone Traversal

In one implementation, upon termination of a base scan routine withinthe first scan zone, the aerial vehicle can navigate along the flightpath from the second waypoint proximal the floor of the first scan zoneto the third waypoint proximal the second scan zone (e.g., under thecanopy of trees to a second scan zone). The aerial vehicle can executeSLAM techniques to follow the flight path, and simultaneously captureimages of the underside of the canopy and the forest floor, such aslocations and widths of tree bases, tree trunks, and other objects; andcollect ambient condition data while navigating from the second waypointto the third waypoint.

In one variation, in response to detecting a density of trees proximalthe first scan zone greater than a threshold density (i.e., the forestis very thick and the aerial vehicle cannot exit the scan zone beneaththe canopy), the aerial vehicle can traverse to the second waypointwithin the first scan zone, vertically beneath the gap in the canopypreviously traversed to enter the first scan zone, and traverse throughthe gap in the canopy. Once above the canopy, the aerial vehicle cannavigate to the second scan zone, identify a gap in the canopy, andtraverse through this gap to scan the second scan zone.

Therefore, the aerial vehicle can utilize onboard optical sensors toautonomously scan tree bases and the underside of the canopy proximalthe aerial vehicle while traversing from a first scan zone to a secondscan zone underneath the canopy. By traversing beneath the canopy, theaerial vehicle can capture images of additional tree bases in locationsoutside the first or second scan zones, and the computer system canfurther derive correlations between these additional images and anoverhead image of trees in the stand. The computer system can analyzethe additional images of tree bases between scan zones to characterizetrends between scan zones. The aerial vehicle can execute anunder-canopy traversal between a first scan zone and a second scan zonewhen brush and tree density conditions are favorable to an under-canopytraversal. In other situations in which a dense forest with manyobstacles (i.e., trees, brush) could slow the progress of the aerialvehicle, the aerial vehicle can autonomously identify a gap in thecanopy, traverse the gap to the area above the scan zone, and travel tothe second scan zone over the canopy.

6. Second Scan Zone

In one implementation, the aerial vehicle can navigate along the flightpath from the second waypoint to the third waypoint proximal a floor ofthe second scan zone (e.g., under the canopy of the trees). The aerialvehicle can then implement the methods and techniques described abovefor the second scan zone. During the base scan through the second scanzone, the aerial vehicle can complete an under-canopy scan to identify agap in the canopy in order to exit the scan zone and continue navigationalong the flight path to a third scan zone. At the completion of thebase scan routine, the aerial vehicle can traverse to a location beneaththe identified gap and ascend in an upward vertical traversal from thethird waypoint to a fourth waypoint above the second scan zone (e.g.,above the canopy of trees). Once above the canopy of trees, the aerialvehicle can execute an overhead canopy scan of the second scan zoneprior to traversing over the canopy to a third scan zone.

In one variation, in response to detecting a density of trees in thecanopy of the second scan zone greater than a threshold density (i.e.,the canopy is very thick, and not enough space exists for the aerialvehicle to exit the scan zone through the canopy), the aerial vehiclecan traverse to a third scan zone under the canopy.

6.1 Under-Canopy Scan

In one implementation, the aerial vehicle can execute an under-canopyscan routine to identify a gap in the canopy in order to exit the scanzone. The aerial vehicle can simultaneously execute the base scanroutine of the second scan zone. In this implementation, the aerialvehicle can: navigate along the flight path through the second scanzone; collect a set of georeferenced upward-facing depth images (i.e.,from the aerial vehicle to the underside of the canopy); and, inresponse to detecting a depth image depicting a distance greater than athreshold distance, flag the location as a potential gap in the canopy.Further, the aerial vehicle can aggregate (i.e., stitch) the set ofupward-facing depth images into a spatial representation of the canopy.

Alternatively, the aerial vehicle can capture georeferenced optical data(i.e., color images, light intensity levels), during the base scanroutine. The aerial vehicle can then access and analyze the images usingcomputer vision techniques and/or color intensity analysis to detect agap in the canopy based on the image.

In one variation, the aerial vehicle can execute a second under-canopyscan routine to identify gaps in the canopy, in addition to the basescan routine.

6.2 Under-Canopy Gap Detection

Generally, during the under-canopy gap scanning procedure, the aerialvehicle can analyze each image and associated depth data in real-timeusing computer vision techniques to identify a gap in the canopy greaterthan the maximum width of the aerial vehicle. The aerial vehicle canalso assemble the set of images into a single two-dimensional compositeimage and can further aggregate the location and depth data associatedwith each image to produce an under-canopy profile (e.g., a spatialrepresentation of the canopy).

In particular, the aerial vehicle can: access a raw two-dimensionalimage depicting the underside of the canopy from below, captured by theaerial vehicle (or a composite two-dimensional image stitched togetherfrom a set of images captured by the aerial vehicle); identify a set ofhigh color intensity value pixels in the image (i.e., filters the imageto identify all pixels within the image with a total color intensityvalue of R, G, and/or B greater than a threshold color intensity value);identify a cluster of high color intensity value pixels within theimage; isolate a set of pixels in the cluster with greatest minimumwidth; detect a location of the set of pixels with the greatest minimumwidth, based on geolocation data associated with the raw two-dimensionalimage; and identify the location of the set of pixels as a gap in thecanopy.

Alternatively, the aerial vehicle can: convert the color intensityvalues to greyscale; identify pixels with a greyscale color intensityvalue greater than a threshold color intensity value; identify clusterof high color intensity value pixels within the image; isolate a set ofpixels in the cluster with greatest minimum width; detect a location ofthe set of pixels with the greatest minimum width, based on geolocationdata associated with the raw two-dimensional image; and identify thelocation of the set of pixels as a gap in the canopy. In thisimplementation, the aerial vehicle can access a static threshold colorintensity value representative of a boundary between light levels abovethe canopy and light levels below the canopy. The static threshold colorintensity value can be pre-loaded to the aerial vehicle prior toexecution of the scan routine. The aerial vehicle can calculate adifference in the color intensity (grayscale) detected by the opticalsensors on the aerial vehicle and the threshold color intensity. Inresponse to detecting the color intensity greater than the thresholdcolor intensity, by greater than a threshold difference, the aerialvehicle can verify the current position above the canopy. Conversely, inresponse to detecting the color intensity less than the threshold colorintensity, by greater than a threshold difference, the aerial vehiclecan verify the current position below the canopy.

In another implementation, the aerial vehicle can apply a dynamicthreshold color intensity value by collecting a sample color intensityvalue when positioned above the canopy and recalibrate the thresholdcolor intensity value prior to each descent through the canopy based onthe collected sample color intensity value. Thus, the aerial vehicle canapply the dynamic threshold color intensity value based on thepreviously recorded conditions above the canopy to increase thesensitivity of the comparison between the color intensity values ofindividual pixels and to adjust the threshold color intensity value to apercentage of the collected sample color intensity value.

For example, the aerial vehicle can initiate the scan routine at thefirst scan zone and collect a first sample color intensity value ofR=64, G=156, B=255, above the canopy corresponding to current conditionsat the first scan zone (e.g., a blue sky). The aerial vehicle can then:execute the scan routine within the first scan zone; calibrate thethreshold color intensity value to ˜90% of the first sample colorintensity value (e.g., R=57, G=140, B=229); and navigate along theflight path to the second scan zone. The aerial vehicle can isolate acluster of pixels with R, G and/or B color intensity greater than thethreshold values and exhibiting a greatest minimum width greater thanthe maximum dimension of the aerial vehicle (e.g., maximum width) andthereby, identify a gap in the canopy within the second scan zone. Theaerial vehicle can then traverse through the gap and initiate anoverhead scan of the canopy of the second scan zone.

The aerial vehicle can then navigate along the flight path over thecanopy to the third scan zone. The aerial vehicle can similarly collecta second sample color intensity value of R=201, G=226, B=255, above thethird scan zone corresponding to current conditions (e.g., an overcastsky). The aerial vehicle can then: descend through a gap in the canopy;execute a scan routine within the third scan zone; and recalibrate thethreshold color intensity value to ˜80% of the second sample colorintensity value (e.g., R=160, G=190, B=204), corresponding to currentconditions above the canopy in the third scan zone. The aerial vehiclecan then navigate along the flight path to a fourth scan zone. Theaerial vehicle can identify a cluster of pixels with R, G and/or B colorintensity greater than the recalibrated threshold values and exhibitinga greatest minimum width greater than the maximum width of the aerialvehicle and thereby identify a gap in the canopy within the fourth scanzone. The aerial vehicle can then navigate through the gap to above thecanopy.

In another implementation, the aerial vehicle can: access the depthimages recorded at locations within the scan zone and assemble (e.g.,stitch) the depth images into a composite depth image of the canopy;identify pixels, within the composite depth image, of infinite depthmagnitude; identify a cluster of pixels of infinite magnitude; isolate aset of pixels in the cluster of greatest minimum width; detect alocation of the set of pixels with the greatest minimum width, based ongeolocation data associated with the depth image; and identify thelocation of the set of pixels as a gap in the canopy.

Alternatively, the aerial vehicle can identify a group of pixels ofinfinite depth magnitude via a LIDAR sensor. The aerial vehicle can theninterpret a pixel of infinite depth magnitude as a potential gap in thecanopy. The aerial vehicle can then identify a cluster of pixels ofinfinite depth magnitude with a greatest minimum width approximating amaximum width of the aerial vehicle as a gap in the canopy.

In another implementation, the aerial vehicle can: traverse to thelocation of a next potential gap in the canopy; activate theupward-facing proximity sensor; and ascend toward the potential gap. Inresponse to a signal from the upward-facing proximity sensor indicatingan obstruction, the aerial vehicle can cease the ascent. Later, thecomputer system can access this location and update the set of locationsin the stand of trees to indicate absence of a gap in the canopy at thisrecorded location. The aerial vehicle can then traverse to a nextlocation of an identified gap in the canopy.

6.3 Vertical Traversal

As shown in FIG. 3B, in response to identifying a gap in the canopy, theaerial vehicle can traverse to a position below the identified gap inthe canopy and ascend toward the gap. During the ascent (e.g., verticaltraversal through the identified gap), the aerial vehicle can: captureimages of tree trunks and other objects proximal the aerial vehicle viathe optical sensor; detect and record a light level and/or a color valuewithin the gap via a light level sensor and/or the optical sensor. Inresponse to detecting a light level exceeding a threshold light leveland/or detecting a color value falling within a threshold difference ofa target color value, the aerial vehicle can identify traversal throughthe gap and above the canopy.

In one variation, the aerial vehicle can continuously detect thedistance between the base of the aerial vehicle and the forest floor viaa distance sensor (e.g., LIDAR) oriented downward; and cease traversalthrough the identified gap in response to the distance between the baseof the aerial vehicle and the forest floor exceeding a thresholddistance.

6.4 Overhead Scan of Second Scan Zone

Generally, upon termination of traversal through the gap in the canopy,the aerial vehicle can initiate an overhead gap scanning procedure abovethe canopy of the second scan zone.

The aerial vehicle can: traverse to the center of the scan zone; accessa nominal flight path along an outward spiral from the center of thescan zone toward the perimeter of the scan zone; collect geolocationdata; capture color images of the canopy below, via the downward-facingoptical sensor; collect depth data via a depth sensor (i.e., LIDAR); andcollect ambient data, such as temperature and/or humidity.

The computer system can then compile the georeferenced base scan datacollected in the second scan zone with the overhead canopy scan data ofthe second scan zone in order to correlate an individual tree basediameter to an individual treetop within the second scan zone (e.g.,matching the location of the tree base identified in the base scan tothe location of the treetop identified in the canopy scan). Inparticular, the computer system can directly detect the height of thetrees proximal the aerial vehicle during the vertical traversal. Thecomputer system can further apply a base-to-height correlation to theremaining treetops identified in the canopy of the second scan zone toextrapolate the height of the trees in the second scan zone.

6.5 Over-Canopy Zone-to-Zone Traversal

In one implementation, as shown in FIG. 3B, at the conclusion of theoverhead scan of the second scan zone, the aerial vehicle can navigatealong the flight path from a current location, over the top of thecanopy of the stand of trees, to a third scan zone. The aerial vehiclecan: traverse from the second scan zone to the third scan zone; monitora current position via the optical sensor; and capture a set ofgeoreferenced high resolution overhead images of the canopy, via theoptical sensor, along the flight path. The aerial vehicle can ceasetraversal in response to the current location corresponding to a set ofgeolocating coordinates assigned to the third scan zone.

7. Other Zones

The aerial vehicle can implement methods and techniques described abovefor each subsequent set of scan zones, under-canopy traversals,over-canopy traversals, and/or vertical traversals in the scan routinewithin the stand of trees, as shown in FIGS. 3A and 3B. The computersystem can additionally integrate strip cruise segments into the nominalflight path of the aerial vehicle to increase total scan coverage.

Generally, the aerial vehicle can access and execute a flight paththrough the array of scan zones within the boundary of the stand oftrees prior to returning to the first waypoint of the flight path. Inone variation, in response to detecting low onboard battery power, theaerial vehicle can return to the first waypoint point; dock at a powerstation proximal the first waypoint to recharge; and initiate a nextflight path through a next stand of trees.

8. Strip Cruise

In one implementation as shown in FIGS. 3A and 3B, the aerial vehiclecan capture a set of color and depth images of trees along a nominallystraight strip cruise of the stand of trees while traversing betweenscan zones (e.g., waypoints of the flight path).

In one variation, the aerial vehicle can capture images of tops of treeswhile traversing a nominally straight strip cruise above the stand oftrees. In this example, the aerial vehicle includes: a first cameramounted to a first side of a lateral axis of the aerial vehicle; asecond camera mounted to a second side of the lateral axis of the aerialvehicle opposite the first side; and a third camera mounted to theaerial vehicle between the first camera and the second camera and facinga floor of the stand of trees. The aerial vehicle can then leverage thefirst camera, the second camera, and the third camera to capture a firstsequence of images of treetops within a threshold distance of the firstside of the aerial vehicle, to capture a second sequence of images oftreetops within the threshold distance of the second side of the aerialvehicle, and to capture a third sequence of images of treetops excludedfrom the field of view of the first camera and the second camera. Thus,the aerial vehicle can traverse along a nominally straight strip cruiseabove the canopy of the stand of trees to avoid collisions withobstacles (e.g., trees, shrubs, branches).

In another variation, the aerial vehicle travels along a scan path belowthe canopy and captures images depicting bases of trees. In thisvariation, the aerial vehicle includes a fourth camera mounted to theaerial vehicle opposite the third camera and facing tops of the stand oftrees. The aerial vehicle can then leverage the first camera, the secondcamera, and the third camera to capture images of bases of trees and thefourth camera to capture images of tops of trees. The computer systemcan then detect obstacles in these images. For example, the computersystem can: access an overhead image depicting the stand of trees;isolate a region of the overhead image depicting the second scan zone;extract a set of visual features from the region of the overhead image;detect a set of objects (e.g., obstacles, shrubs, trees, leaves) in thesecond scan zone based on the set of visual features; and, in responseto detecting the set of objects within a threshold distance of thesecond waypoint of the flight path, define a sixth waypoint between thesecond waypoint and the third waypoint of the flight path; and updatethe flight path to include the sixth waypoint to avoid collision withthe set of objects.

Thus, the aerial vehicle can execute a nominally straight strip cruisebelow the canopy of the stand of trees and leverage the set of camerasto avoid collision with obstacles (e.g., trees, shrubs, branches) whiletraversing between scan zones.

8.1 Boustrophedonic Strip Cruise

In one implementation, the computer system can define a boustrophedonicstrip cruise for execution by the aerial vehicle above the stand oftrees and upload the boustrophedonic strip cruise to the aerial vehicle.In particular, the computer system can: define a first orientation for afirst raster leg across (e.g., vertically across) a first scan zone;define a second orientation orthogonal to the first orientation for asecond raster leg across (e.g., laterally across) the first scan zone;define a third orientation orthogonal to the second orientation andopposite the first raster leg for a third raster leg across (e.g.,vertically across) the first scan zone; and aggregate the first rasterleg, the second raster leg, and the third raster leg into aboustrophedonic strip cruise for execution by the aerial vehicle.

In one variation, the aerial vehicle can execute the boustrophedonicstrip cruise such that the first pass along the first raster leg and thethird pass along the third raster leg overlap. In this variation, thecomputer system can adjust the second raster leg between the firstraster leg and the third raster leg to enable the aerial vehicle tocapture images of trees within the first scan zone between the firstpass and the second pass.

For example, the aerial vehicle can: access the boustrophedonic stripcruise for the first scan zone; execute a first pass at a firstorientation along the first raster leg (e.g., along a nominally straightpath parallel and proximal to a first edge of the stand of trees);execute a second pass at a second orientation orthogonal to the firstorientation (e.g., a first 90 degree turn) along the second raster leg(e.g., along a nominally straight path orthogonal to the first edge ofthe stand of trees); and execute a third pass at a third orientationorthogonal to the second orientation (e.g., a second 90 degree turn) andopposite the first raster leg along the third raster leg (e.g., along anominally straight path parallel and opposite to the first raster leg).The computer system can then access a set of images—depicting tops oftrees within the first scan zone—captured by the aerial vehicle whilenavigating the boustrophedonic strip cruise.

Additionally or alternatively, the computer system can adjust theboustrophedonic strip cruise for the stand of trees to define a gap,containing a set of trees excluded from the strip cruise, between afirst scan zone and a second scan zone. For example, the aerial vehiclecan access the boustrophedonic strip cruise for the stand of trees;execute a first pass at a first orientation along the first raster legof a first scan zone; execute a second pass at a second orientationorthogonal to the first orientation along the second raster leg of theset of trees excluded from the strip cruise; and execute a third passalong the third raster leg at a third orientation orthogonal to thesecond orientation and opposite the first raster leg of the second scanzone. The computer system can then access a set of images—depicting topsof trees within the first scan zone and the second scan zone—captured bythe aerial vehicle while navigating the boustrophedonic strip cruise.

Therefore, the computer system can define a boustrophedonic strip cruisefor execution by the aerial vehicle to capture images depicting tops oftrees within a scan zone and/or to capture images depicting tops oftrees within two scan zones.

9. Data Processing

Generally, the computer system can access optical data (e.g., colorimages and depth images) and non-optical data (e.g., ambient conditiondata) captured by the aerial vehicle while navigating along the flightpath through the stand of trees in real-time during the flight pathand/or upon termination of the flight path. The computer system can thenmanipulate these optical and non-optical data: to derive two-dimensionaland/or three-dimensional representations of each scan zone; to assemblethese representations into a two-dimensional and/or three-dimensionalrepresentation of the stand of trees; to derive correlations betweentree characteristics of each scan zone; to interpolate treecharacteristics of the stand of trees; and to compile treecharacteristics into a virtual representation of tree characteristicsacross the stand of trees.

In one implementation, the computer system can compile images capturedby the aerial vehicle while navigating the flight path through the standof trees into a composite image of each scan zone and/or compile depthimages captured by the aerial vehicle and combine these depth imageswith the color images to assemble a color three-dimensionalrepresentation of each scan zone.

9.1 First Scan Zone: Three-Dimensional Representation

In one implementation, the computer system can compile collected datafrom a first scan zone with an overhead image to derive correlationsbetween the data collected within the first scan zone by the aerialvehicle below the canopy, and the features depicted in a region of theoverhead image representing the first set of trees in the first scanzone.

In one variation, the computer system can access raw two-dimensionalimages and depth images of the canopy of the first set of trees withinthe first scan zone captured by aerial vehicle above the first scan zoneand stitch these raw two-dimensional images together into a compositetwo-dimensional image of the canopy for the first scan zone. Thecomputer system can then layer depth images onto the compositetwo-dimensional image to create a spatial representation of the canopyfrom an overhead perspective (e.g., tops of the first set of trees). Thecomputer system can similarly stitch raw two-dimensional images of theunderside of the canopy captured by the aerial vehicle into a compositetwo-dimensional image of the canopy from the underside perspective andlayer additional depth images to construct a spatial representation ofthe canopy from an under-canopy perspective. The computer system canthen: combine the spatial representation from the overhead perspectiveand the spatial representation from the under-canopy perspective toconstruct a spatial representation of the canopy of the first scan zone;and combine the spatial representation with depth images to construct acolor three-dimensional representation of the first scan zone. Further,the computer system can layer ambient condition data (i.e., temperature,humidity, light level) onto the three-dimensional representation of thefirst scan zone.

In another variation, for the first zone, the computer system: compilesthe set of color images and depth images collected by the aerial vehiclein the first scan zone from both above and below the canopy to assemblethe three-dimensional color representation of: tops of trees; partialgaps extending downwardly from the canopy; sizes of trees facing the gaptraversed by the aerial vehicle; partial gaps extending upwardly intothe canopy from the underside; and/or tree bases and forest floor nearunder-canopy paths traversed by the aerial vehicle.

For example, the computer system can: access a first sequence of imagesrepresenting tops of the first set of trees and captured by the aerialvehicle proximal the first waypoint (e.g., within a threshold distanceof the first waypoint); access an intermediate vertical sequence ofimages representing trunks of the first set of trees and captured by theaerial vehicle while navigating from the first waypoint to the secondwaypoint (e.g., during the downward vertical traversal); and access asecond sequence of images representing bases of the first set of treesand captured by the aerial vehicle proximal the second waypoint at asecond altitude near the floor of the first scan zone. The computersystem can then assemble the first sequence of images, the secondsequence of images, and the intermediate vertical sequence of imagesinto a three-dimensional representation of the first scan zone.

Additionally, the computer system can augment the three-dimensionalrepresentation of the first scan zone by annotating the representationwith depth images and ambient data (e.g., temperature, humidity, lightlevel) collected by the aerial vehicle during the flight path. Forexample, the computer system can access a first sequence of depth imagesrepresenting tops of the first set of trees within the first scan zone;a second sequence of depth images representing bases of the first set oftrees; and an intermediate vertical sequence of depth imagesrepresenting trunks of the first set of trees and captured by the aerialvehicle while navigating along the flight path from the first waypointto the second waypoint. The computer system can then compile the firstsequence of images, the second sequence of images, the intermediatevertical sequence of images, the first sequence of depth images, secondsequence of depth images, and the first intermediate vertical sequenceof depth images into a color three-dimensional representation of thefirst scan zone.

Alternatively, the computer system can assemble ambient data into asimilar first ambient condition representation for the first scan zoneor store ambient data in an ambient data layer in the representation ofthe first scan zone. The representation of the first scan zone can alsodefine an assembly of a cylinder containing the first scan zone. Thecomputer system can then populate the cylinder with optical data (e.g.,depth, and RGB data) collected by the aerial vehicle and store thispopulated cylinder as a first representation defining a georeferencedthree-dimensional point cloud representing trees and tree features ofthe first set of trees within the first scan zone.

Therefore, the computer system can aggregate images captured by theaerial vehicle to generate a three-dimensional representation of thefirst scan zone and annotate the three-dimensional representation withdepth images and ambient condition data. Additionally, the computersystem can augment the spatial representation with an overhead image ofthe scan zone (e.g., a satellite image, an aerial image) to derivecorrelations between the overhead image depicting the tops of treeswithin the scan zone, the sequences of images, and ambient conditiondata captured by the aerial vehicle below the canopy.

9.2 Virtual Representation of Tree Characteristics

In one implementation, the computer system can access sequences ofimages captured by the aerial vehicle while navigating along the flightpath, extract tree characteristics from these sequences of images,interpolate tree characteristics of trees between scan zones (e.g.,trees excluded from the array of scan zones, unscanned trees) andcompile these tree characteristics into a virtual representation of treecharacteristics across the stand of trees.

In one variation, the computer system can access color images capturedby the aerial vehicle representing tops of trees (e.g., above thecanopy) and bases of trees (i.e., below the canopy) within each scanzone. The computer system can then leverage tree canopy characteristicsand lower tree characteristics detected in these color images toestimate tree characteristics of a set of trees excluded from the arrayof scan zones.

For example, the computer system can: access a first sequence of imagesrepresenting tops of a first set of trees within the first scan zone andcaptured by the aerial vehicle proximal the first waypoint; access asecond sequence of images representing bases of the first set of treesand captured by the aerial vehicle proximal the second waypoint; accessa third sequence of images representing bases of a second set of treeswithin the second scan zone and captured by the aerial vehicle proximalthe third waypoint; access a fourth sequence of images representing topsof the second set of trees and captured by the aerial vehicle proximalthe fourth waypoint; and a fifth sequence of images representing tops ofa third set of trees within a third scan zone and captured by the aerialvehicle proximal the fifth waypoint.

The computer system can then: interpolate a first set of tree canopycharacteristics of a fourth set of trees between the first scan zone andthe second scan zone based on visual features detected in the firstsequence of images and the fourth sequence of images; interpolate afirst set of lower tree characteristics of the fourth set of treesbetween the first scan zone and the second scan zone based on visualfeatures detected in the second sequence of images and the thirdsequence of images; interpolate a second set of tree canopycharacteristics of a fifth set of trees between the second scan zone andthe third scan zone based on visual features detected in the fourthsequence of images and the fifth sequence of images; and compile thefirst set of tree canopy characteristics, the first set of lower treecharacteristics, and the second set of tree canopy characteristics intoa virtual representation of tree characteristics across the stand oftrees.

Additionally, the computer system can access color images captured bythe aerial vehicle during upward and downward traversals of gaps in thecanopy of the stand of trees and representing trunks of trees withineach scan zone. The computer system can then estimate tree trunkcharacteristics of a set of trees between the first scan zone and thesecond scan zone (e.g., unscanned trees).

For example, the computer system can: access a first intermediatevertical sequence of images representing trunks of the first set oftrees within the first scan zone and captured by the aerial vehiclewhile navigating from the first waypoint to the second waypoint of theflight path; and access a second intermediate vertical sequence ofimages representing trunks of the second set of trees within the secondscan zone captured by the aerial vehicle while navigating from the thirdwaypoint to the fourth waypoint of the flight path. The computer systemcan then: interpolate a first set of tree trunk characteristics of a setof trees between the first scan zone and the second scan zone (e.g.,unscanned trees) based on visual features detected in the firstintermediate vertical sequence of images and the second intermediatevertical sequence of images; and compile the first set of tree canopycharacteristics, the first set of lower tree characteristics, the firstset of tree trunk characteristics, and the second set of tree canopycharacteristics into the virtual representation of tree characteristicsacross the stand of trees.

Alternatively, the computer system can access intermediate horizontalimages representing trunks of the set of trees between the first scanzone and the second scan zone and estimate a second set of tree trunkcharacteristics of the set of trees between the first scan zone and thesecond scan zone (e.g., unscanned trees) based on visual featuresdetected in these intermediate horizontal images. The computer systemcan then implement methods and techniques described above to compile thefirst set of tree canopy characteristics, the first set of lower treecharacteristics, the second set of tree trunk characteristics, and thesecond set of tree canopy characteristics into the virtualrepresentation of tree characteristics across the stand of trees.

Therefore, the computer system can access color images captured by theaerial vehicle while navigating along the flight path to estimate treecharacteristics of trees excluded from the array of scan zones (e.g.,unscanned trees) and assemble a virtual representation of treecharacteristics across the stand of trees.

9.3 Tree Characteristic Modeling

In one implementation, the computer system can extract tree canopycharacteristics and lower tree characteristics from images of each scanzone and generate tree characteristic models linking canopycharacteristics to lower tree characteristics for each scan zone. Thecomputer system can then leverage the overhead image and these treecharacteristic models to assemble the virtual representation of treecharacteristics across the stand of trees.

In one variation, the computer system can implement computer visiontechniques to identify features in a region of the overhead imagerepresenting tops of the first set of trees in the first scan zone. Thecomputer system can then derive correlations between these detected topsof the first set of trees from the overhead image to characteristics oftrees extracted from sequences of images captured by the aerial vehiclewhile navigating along the flight path. The computer system can thenextrapolate the correlations to construct the representation.

For example, for the first scan zone, the computer system can: access afirst sequence of images representing tops of the first set of treeswithin the first scan zone; extract a set of tree canopy characteristicsof the first set of trees from the first sequence of images; access asecond sequence of images representing bases of the first set of treeswithin the first scan zone; extract a set of lower tree characteristicsof the first set of trees from the second sequence of images; andgenerate a first tree model of the first scan zone linking the set oftree canopy characteristics and the set of lower tree characteristics ofthe first set of trees.

Then, for the second scan zone, the computer system can: access a thirdsequence of images representing bases of the second set of trees withinthe second scan zone; extract a set of lower tree characteristics of thesecond set of trees from the third sequence of images; access a fourthsequence of images representing tops of the second set of trees withinthe second scan zone; extract a set of tree canopy characteristics ofthe second set of trees from the fourth sequence of images; and generatea second tree model of the second scan zone linking the set of lowertree characteristics and the set of tree canopy characteristics of thesecond set of trees. The computer system can then: access an overheadimage depicting the stand of trees; interpolate a set of total treecharacteristics of the stand of trees based on visual features detectedin the overhead image, the first tree model of the first scan zone, andthe second tree model of the second scan zone; and compile the set oftotal tree characteristics into the virtual representation of treecharacteristics across the stand of trees.

In one variation, the computer system can extract tree canopycharacteristics and base diameters of trees from images of each scanzone and generate a tree characteristics model linking tree canopycharacteristics to base diameters for each scan zone. The computersystem can then leverage the overhead image and this treecharacteristics model to estimate base diameters of trees excluded fromthe array of scan zones (e.g., unscanned trees).

For example, for the first scan zone, the computer system can: access afirst sequence of images representing tops of the first set of treeswithin the first scan zone; extract a set of tree canopy characteristicsof the first set of trees from the first sequence of images; access asecond sequence of images representing bases of the first set of treeswithin the first scan zone; extract a set of lower tree characteristicsof the first set of trees from the second sequence of images; derive aset of base diameters of the first set of trees based on the set oflower tree characteristics; and generate a tree characteristic model ofthe first scan zone linking the set of tree canopy characteristics andthe set of base diameters of the first set of trees. The computer systemcan then: access an overhead image depicting the stand of trees; isolatea region of the overhead image depicting a set of trees excluded fromthe array of scan zones; extract a set of visual features of the set oftrees from the overhead image; estimate a set of tree base diameters ofthe set of trees—excluded from the array of scan zones—based on the setof visual features and the tree characteristics model.

Additionally or alternatively, the computer system can generate a treecharacteristics model linking tree canopy characteristics to the virtualrepresentation of tree characteristics across the stand of trees andthen leverage this model and the overhead image to estimate tree canopycharacteristics of a set of trees excluded from the array of scan zones.In this variation, the computer system can: generate a treecharacteristics model linking the set of tree canopy characteristics ofthe first set of trees within the first scan zone and the virtualrepresentation of tree characteristics across the stand of trees; accessthe overhead image depicting the stand of trees; and interpolate a setof tree canopy characteristics of the set of trees—excluded from thearray of scan zones—based on the tree characteristics model and visualfeatures detected in the overhead image; and compile this set of treecanopy characteristics into the virtual representation of treecharacteristics across the stand of trees.

Therefore, the computer system can generate tree characteristics modelslinking canopy tree characteristics and lower tree characteristicsand/or base diameters to estimate analogous tree characteristics oftrees excluded from the array of scan zones (e.g., unscanned trees).Additionally, the computer system can render the set of tree basediameters of trees for presentation within a user portal, as furtherdescribed below.

9.4 First Scan Zone: Intrinsic Characteristics

In one implementation, the computer system implements artificialintelligence and/or computer vision techniques to detect characteristicsof trees in the first scan zone based on images captured by the aerialvehicle while traversing overhead, vertically through, and around basesof trees in the first scan zone.

In one variation, the computer system can access optical data andimplements computer vision techniques, spectral analysis and/or coloranalysis to identify intrinsic characteristics of the trees in the firstzone such as: the location and size of a tree base; the location of atree top; tree height; canopy geometry; forks in a particular tree;color of tree trunks and branches; foliage color, foliage type (i.e.,leave or needle shape and configuration) and foliage density; pests; andforest floor brush density. The computer system can then annotate thefirst three-dimensional representation with the intrinsiccharacteristics of trees (and forest floor brush) in the first scanzone. The computer system can also store the intrinsic characteristicsof the first scan zone in an intrinsic characteristics layer of thefirst three-dimensional representation.

Therefore, the computer system can: detect intrinsic characteristics oftrees depicted in the set of images collected by the aerial vehicle;annotate the spatial representation of the first scan zone with theintrinsic characteristics; and derive correlations based on theannotated spatial representation to predict other characteristics oftrees within the scan zone.

9.5 First Scan Zone: Intra-Zone Characteristic Interpolation

In one implementation, the computer system can implement artificialintelligence, machine learning, and/or regression techniques to derivecorrelations between data elements such as: lower tree characteristics(e.g., tree base, trunk) and top characteristics to construct apredictive zone model of the first scan zone. Generally, the location ofthe base of a tree is the anchor for extrapolation or interpolation ofother tree characteristics.

In particular, based on data collected by the aerial vehicle and theintrinsic characteristics derived via data analysis, the computer systemcan extrapolate characteristics of an individual tree from a set ofcritical intrinsic data points including: tree base location, tree basediameter, and tree height. Based on direct detection of a subset ofcritical intrinsic data points for a particular tree in the first scanzone, the computer system can extrapolate critical data points foradditional trees within and proximal the first scan zone from a singlecritical intrinsic data point. Further, the computer system cancalculate a base-to-height ratio for trees located in the first scanzone based on direct detection of a tree base diameter and a tree heightof a tree (i.e., a representative tree) within the scan zone. Thecomputer system can then apply the base-to-height ratio to additionaltrees within and proximal the first scan zone to predict tree base ortree height based on detection of either a tree base diameter or treeheight. Alternatively, the computer system can apply the base-to-heightratio to an average height to calculate average base diameter andvice-versa.

For example, the computer system can: access a first sequence of imagesrepresenting tops of a first set of trees within the first scan zone andcaptured by the aerial vehicle proximal the first waypoint; extract afirst set of visual features from the first sequence of images; derive afirst set of heights of the first set of trees, based on the first setof visual features; access a second sequence of images representingbases of the first set of trees within the first scan zone and capturedby the aerial vehicle proximal the second waypoint; extract a second setof visual features from the second sequence of images; derive a firstset of base diameters of the first set of trees based on the second setof visual features; and calculate a tree-base-to-height ratio based on afirst combination of the first set of heights (e.g., average height ofthe first set of trees) and a second combination of the first set ofdiameters (e.g., average base diameter of the first set of trees).

The computer system can then implement methods and techniques describedabove to estimate tree canopy characteristics and lower treecharacteristics of a set of trees between the first scan zone and thesecond scan zone, excluded from the array of scan zones. The computersystem can: apply the tree-base-to-height ratio to the canopycharacteristics of the set of unscanned trees; and, in response to thecanopy characteristics corresponding to the tree-base-to-height ratio,predict a set of heights of the set of unscanned trees. Similarly, thecomputer system can: apply the tree-base-to-height ratio to the lowertree characteristics of the set of unscanned trees; and, in response tothe lower tree characteristics corresponding to the tree-base-to-heightratio, predict a set of base diameters of the set of unscanned trees.

Additionally, a set of secondary intrinsic data points either directlydetected by the aerial vehicle or derived by the computer system viaanalysis can increase the accuracy of extrapolated data points by thecomputer system. Secondary intrinsic data points can include: treehealth (i.e., absence of pests, abnormal growth, discoloration);species; forks; foliage density; and foliage damage. The computer systemcan fuse these data with the three-dimensional representation of thefirst scan zone to generate a virtual representation of the first scanzone.

In one variation, the computer system can identify a treetop within thefirst scan zone in an image captured by the aerial vehicle, via imageanalysis. The computer system can then leverage the tree characteristicsmodels and the three-dimensional representation of the first scan zoneto predict the tree base location, tree size (base diameter, height),foliage density, and health of a tree within the first scan zonerepresented in the image by the treetop. The computer system can alsoidentify a base of the tree within the first scan zone in an imagecaptured by the aerial vehicle, via image analysis. The computer systemcan predict the tree top location, tree size (height), foliage density,and health of the tree represented in the image by the base of the tree.The computer system can identify the base size (diameter) in the imageand apply the additional datapoint of base diameter to increasepredictive accuracy. The computer system can also identify a side view(i.e., a trunk) of a tree within the first scan zone in an imagecaptured by the aerial vehicle, via image analysis. The computer systemcan leverage the tree characteristics models and the three-dimensionalrepresentation of the first scan zone to predict the base location, treetop location, tree size (height), foliage density, and health of thetree represented in the image by the side view. The computer system canalso identify the width (i.e., diameter) of the trunk depicted in theimage and apply the additional datapoint of trunk width to increasepredictive accuracy.

Therefore, for the first zone, the computer system can access datacollected by the aerial vehicle, process and analyze the data to detectall treetops within the first scan zone and leverage the treecharacteristics models and the three-dimensional representation of thefirst scan zone to predict: tree base location associated with eachtreetop; tree base diameter; tree height; tree health; tree species;foliage density, and other characteristics.

10. Second Scan Zone: Image Aggregation

In one implementation, the computer system can implement methods andtechniques described above to assemble a second three-dimensionalrepresentation of: treetops; partial gaps extending downwardly from thecanopy; sizes of trees facing the gap traversed by the aerial vehicle;partial gaps extending upwardly into the canopy from the underside;and/or tree bases and forest floor near under-canopy paths traversed bythe aerial vehicle for the second scan zone.

In one variation, the computer system can: access a first sequence ofimages representing bases of the second set of trees and captured by theaerial vehicle proximal the third waypoint (e.g., within a thresholddistance of the third waypoint) at an altitude near the floor of thesecond scan zone; access an intermediate vertical sequence of imagesrepresenting trunks of the second set of trees and captured by theaerial vehicle while navigating from the third waypoint to a fourthwaypoint (e.g., during the upward vertical traversal); and access asecond sequence of images representing tops of the second set of treesand captured by the aerial vehicle proximal the fourth waypoint at analtitude above the second set of trees. The computer system can thenassemble the first sequence of images, the second sequence of images,and the intermediate vertical sequence of images into athree-dimensional representation of the second scan zone.

11. Other Zones

Generally, the computer system can implement methods and techniquesdescribed above for each other scan zone in the stand of trees togenerate a predictive zone model for each scan zone. Additionally, thecomputer system can annotate the predictive zone model with intrinsiccharacteristics particular to the scan zone, and/or annotate withinterpolated characteristics derived from adjacent scan zones. Inparticular, the computer system can annotate the three-dimensionalrepresentation of the each scan zone with images and characteristics ofindividual trees (i.e., “representative trees”), captured by the aerialvehicle.

In one implementation, the computer system can assemble thethree-dimensional representation of the first scan zone, thethree-dimensional representation of the second scan zone, and thethree-dimensional representation of each other scan zone into athree-dimensional representation of the stand of trees. The computersystem can then leverage this three-dimensional representation of thestand of trees and tree characteristic models to interpolate canopycharacteristics of unscanned trees between scan zones.

For example, the computer system can implement methods and techniquesdescribed above to estimate a first set of tree canopy characteristicsof a set of unscanned trees between the first scan zone and the secondscan zone. The computer system can then: extract a second set of treecanopy characteristics of the first set of trees from images depictingtops of the first set of trees within the first scan zone; extract athird set of tree canopy characteristics from images depicting tops ofthe second set of trees within the second scan zone; and generate a treecharacteristics model linking the first set of tree canopycharacteristics, the second set of tree canopy characteristics, thethird set of tree canopy characteristics, and the three-dimensionalrepresentation of the stand of trees.

The computer system can: isolate a region of the overhead imagerepresenting another set of unscanned trees (e.g., excluded from thefirst scan zone and the second scan zone); extract a set of treecharacteristics of this set of unscanned trees from the region of theoverhead image; interpolate a fourth set of tree canopy characteristicsof this set of unscanned trees based on the tree characteristics modeland the set of tree characteristics; and compile the first set of treecanopy characteristics, the second set of tree canopy characteristics,the third set of tree canopy characteristics, and the fourth set of treecanopy characteristics into the virtual representation of treecharacteristics across the stand of trees.

Therefore, the computer system can combine three-dimensionalrepresentations of each scan zone into a three-dimensionalrepresentation of the stand of trees. Additionally, the computer systemcan leverage tree characteristics models, the overhead image of thestand of trees, and ambient condition data to assemble a virtualrepresentation of tree characteristics across the stand of trees.

12. Strip Modeling

In one implementation, the computer system receives georeferenced data(e.g., tree base locations, tree base sizes, tree color, tree pattern,and foliage density) collected by the aerial vehicle when traversingbetween a first scan zone and a second zone under the canopy. The aerialvehicle collects data within a strip with a width extending laterallyfrom the left and right sides of the aerial vehicle, perpendicular tothe direction of travel of the aerial vehicle and extending in lengthbetween the first scan zone and the second scan zone.

The computer system retrieves the georeferenced overhead image (e.g., alow-resolution overhead satellite or aerial image of the stand of trees,an overhead image captured by the aerial vehicle prior to the flightpath of the stand of trees) and detects a region of the overhead imagecorresponding to the strip traversed by the aerial vehicle and extractsvisual features from the overhead image. The computer system thenderives correlations between these visual features from the region ofthe overhead image and the data collected by the aerial vehicle duringtraversal of the strip to create a spatial strip model between the firstscan zone and the second scan zone. The computer system can then applythe spatial strip model to derive characteristics of trees (e.g., treebase location, tree height) within the strip based on the visualfeatures (e.g., tops of trees) extracted from the region of the overheadimage. Additionally, the computer system can extrapolate thecharacteristics of trees detected within the overhead image adjacent tothe strip by applying the spatial strip model based on tops of treesidentified adjacent to the strip within the overhead image.

For example, during the traverse between the first scan zone and thesecond scan zone, the aerial vehicle captures a first georeferencedimage depicting a tree base. The computer system can then: access theimage; identify a base location and a base diameter of the tree depictedin the image; access coordinates of the tree base based on thegeoreferenced image; and access the overhead image of the stand oftrees. The computer system can identify the region of the overhead imageextending from the first scan zone to the second scan zone based on thegeoreferenced coordinates of the first scan zone and the second scanzone. The computer system can then isolate visual features proximal thecoordinates of the tree base and leverage these coordinates to isolate acorresponding top of the tree. The computer system can repeat thisprocess for additional trees identified within the strip between thefirst scan zone and the second scan zone.

In one implementation, the computer system can apply a combination ofthe three-dimensional representation of the first scan zone and thethree-dimensional representation of the second scan zone (e.g., agradient of characteristics between the first scan zone and the secondscan zone) to create a theoretical predictive strip representationbetween the first scan zone and the second scan zone. In one variation,the computer system can validate the theoretical predictive striprepresentation based on data collected by the aerial vehicle during anunder-canopy traversal between the first scan zone and the second scanzone.

Therefore, the computer system can generate a spatial representation ofareas between scan zones directly from images and ambient condition dataon-the-fly while traversing between scan zones, and/or generatetheoretical representations of areas between scan zones by interpolatingcharacteristics between scan zones.

13. Stand Model

In one variation, the computer system can compile the georeferencedrepresentations and full overhead scan into a sparse three-dimensionalrepresentation of the stand of trees. The computer system can compilethe set of color images and the set of depth images collected by theaerial vehicle, including: the identified treetops and canopy gaps; treebases from each zone; tree trunks, branches, and foliage from eachvertical traversal completed in each scan zone; and the treetopsidentified in the full overhead image; to construct a spatialrepresentation of the stand of trees limited to the data collected bythe aerial vehicle and the features depicted in the full overhead image.The computer system can then implement artificial intelligence, machinelearning, and/or computer vision techniques to derive correlationsbetween data elements and generate a predictive stand model from thedata collected by the aerial vehicle. The computer system can leveragethe predictive stand model to predict characteristics of trees (e.g.,base width, height) based on the features detected in the full overheadimage, such as treetops. The computer system can then apply thepredictive stand model to interpolate tree characteristics and augmentthe gaps in the sparse three-dimensional representation of the stand oftrees to generate a synthetic three-dimensional representation of thestand of trees.

In one implementation, the computer system can detect individualtreetops in the full overhead scan. For each detected treetop, thecomputer system can implement the synthetic spatial representation topredict: the tree base location; the tree base diameter; and the treeheight. Additionally, the computer system can predict secondaryinformation such as: tree species, foliage density, health, andprobability of forks within the tree. The computer system can predicttree characteristics based on the full overhead scan with varyingdegrees of confidence, based on the proximity of the detected feature(i.e., treetop) to a scan zone.

For example, the sparse three-dimensional representation of the stand oftrees includes descriptive data of individual trees and intrinsic datacollected by the aerial vehicle. The computer system can apply thepredictive model to extrapolate tree characteristics across the stand oftrees, generating the synthetic spatial representation. As shown in FIG.3A, the synthetic spatial representation exhibits higher predictiveconfidence within the area of the scan zones 1-9 and lower predictiveconfidence in unscanned areas of the stand of trees (i.e., interpolationareas I1-I4.)

In one variation, the aerial vehicle can complete a series of stripcruise scans and the computer system can apply the predictive stripmodel to the strip cruising sections (FIG. 3A, traversals T1-T8)exhibiting a medial predictive confidence (e.g., between the predictiveconfidence of the scan zones and interpolation areas) within thesynthetic spatial representation.

In another variation, the computer system represents the predictiveconfidence as a gradient, with the highest confidence at the location ofa scan zone (or an individually scanned, or “representative” tree) andthe lowest confidence at the centroid of adjacent scan zones.

For example, the computer system can access an overhead image of thestand of trees and virtually overlay the synthetic spatialrepresentation based on georeferenced coordinates. The computer systemcan then access a count of trees in each scan zone (e.g., a quantity oftrees) and match the count to the count of treetops depicted in eachzone in the overhead image exhibiting a match percentage greater than athreshold match percentage (e.g., 92%), resulting in a high modelpredictive confidence within the scan zones. The computer system cancomplete similar steps for each strip traversed between each zone, witha lower match success percentage, resulting in a lower model predictiveconfidence within the strips. The computer system can then calculate acount of trees in the unscanned areas of the tree stand (FIG. 3A, I1-I4)based on the treetops depicted in the overhead image with no matchpercentage (as there is no collected data to match to), resulting in alower model predictive confidence than the model predictive confidencewithin a strip. The computer system can also extrapolate treecharacteristics such as height, base size, and timber volume via thesynthetic spatial representation within each scan zone and strip withsimilar confidence levels. The computer system can extrapolate treecharacteristics of each treetop detected in the overhead image based onthe best available model data. In particular, the computer system canpredict tree characteristics of individual trees within a segment of anoverhead image representing a scan zone based on treetops detected inthe segment of the image and derive correlations to the set of imagescollected within the scan zone; or based on the closest representativetree. Additionally, the computer system can predict tree characteristicscorresponding to individual trees detected in regions of the overheadimage depicting areas outside a scan zone by extrapolating treecharacteristics of the closest scan zone or representative tree, orextrapolating a gradient of characteristic values between a first scanzone including a first set of tree characteristics, and a second scanzone including a second set of tree characteristics.

Further, the computer system can layer additional detected or derivedinformation about the stand of trees into the predictive stand modelsuch as: pest infestation or disease, density of trees, speciesdistribution, fire risk, ease of access based on terrain condition, andothers.

Therefore, the computer system can construct a representative syntheticspatial representation of the stand of trees to predict characteristicsof trees within the stand with varying confidence levels based on anindividual tree's proximity to a scan zone.

14. Metrics

Generally, the computer system can extract a set of metrics from thethree-dimensional representation of the stand of trees including: treecount, species, timber volume, and health metrics of the stand of trees.In particular, the computer system can extract a gross tree count fromthe three-dimensional representation of the stand of trees and segmentthe count into metric categories such as healthy mature trees, healthyimmature trees, unhealthy trees, harvest-ready trees, etc.

Furthermore, the computer system can segment the tree count by treespecies using secondary data in the synthetic three-dimensionalrepresentation of the stand of trees to produce a species histogram andthereby, enable a stand manager or owner to visualize the timber contentof the stand of trees. The computer system can also calculate additionalmetrics from the images and non-optical data collected by the aerialvehicle and the three-dimensional representation including board feet ofa particular species of timber, or carbon capture and/or sequestrationvalues of the stand of trees, based on the count, age, and size of treespresent in the stand.

In one implementation, the computer system can identify a particulartree species within a first scan zone within the stand model byextracting from the stand model (i.e., from the set of images capturedby the aerial vehicle) a set of characteristics of the particular treewithin the first scan zone. The computer system then accesses a set oftemplate characteristics associated with a tree type and computer visiontechniques (e.g., template matching) to match the detected features ofthe particular tree to a set of template characteristics associated witha tree type. In response to the detected characteristics of theparticular tree matching the template characteristics within a matchingthreshold, the computer system assigns the particular tree the tree typeassociated with the template characteristics. The computer system canapply the same methods to assign tree types to particular trees acrossthe stand model. The computer system can then interpolate tree types forother trees within the stand model not directly detected by the aerialvehicle to further complete the stand model.

In another implementation, the computer system can construct an audittool: defining or linked to a database and a user interface; configuredto interpret data contained in the three-dimensional representation ofthe stand of trees and configured to render thisrepresentation—annotated with metrics—within a user portal. The computersystem can aggregate data collected from a scan zone of a stand of treesand/or scan data collected from the array of scan zones of the stand oftrees to augment or enhance the three-dimensional representation of thestand of trees.

In one variation, the computer system can access an initial set ofimages representing tops of the stand of trees captured by the aerialvehicle and compile these images into a composite aerial image depictingthe stand of trees, the composite image characterized by a firstresolution (e.g., low-resolution overhead image). The computer systemcan then access a set of ground images captured by the aerial vehicleand characterized by a second resolution greater than the firstresolution (e.g., high-resolution images representing bases of the standof trees). In this variation, the computer system can transform visualfeatures extracted from these ground images and the overhead image intoa map of the stand of trees for presentation to a user.

For example, the computer system can: compile the set of ground images—representing bases of the stand of trees—and visual features extractedfrom these ground images and the overhead image of the stand of treesinto a map of the stand of trees; annotate the map of the stand of treeswith values of a metric for a first set of trees depicted in the set ofground images; annotate the map of the stand of trees with values of themetric for a second set of trees depicted in the overhead image; compilevalues of the metric, for the first set of trees and the second set oftrees, within the map of the stand of trees into a composite value ofthe metric; and render the map of the stand of trees within the userportal.

14.1 Composite Value+Metric Function

Generally, the computer system can implement computer vision techniques(e.g., object detection, edge detection, template matching) to detectvisual features (e.g., bark characteristics, pixel width, foliagecharacteristics) of each tree depicted in a high-resolution ground imageand characterize a value of a metric associated with these visualfeatures such as: tree type; tree species; pest risk; base diameter;fire risk; defects; carbon capture; etc. The computer system can thengenerate a metric function representing a correlation between values ofthis metric and these visual features and leverage the metric functionto characterize a value of the metric of each tree depicted in thelow-resolution overhead image of the stand of trees.

In one implementation, the computer system can: access a set of groundimages representing bases of the stand of trees and captured by anaerial vehicle; and isolate a first set of trees depicted in the set ofground images. Then, for each tree in the first set of trees, thecomputer system can: detect a first region of the set of ground imagesdepicting the tree; extract a first set of visual features from thefirst region of the set of ground images; characterize a value of ametric of the tree based on the first set of visual features; detect asecond region of the overhead image depicting the tree; extract a secondset of visual features from the second region of the overhead image; andstore the value of the metric and the second set of visual features in acontainer in a set of containers. The computer system can then: generatea metric function representing a correlation between values of themetric and visual features within the stand of trees based on the set ofcontainers.

Furthermore, the computer system can: access an overhead image depictingthe stand of trees; and isolate a second set of trees, excluded from theset of ground images, depicted in the overhead image. Then, for eachtree in the second set of trees, the computer system can: detect a thirdregion of the overhead image depicting the tree; extract a third set ofvisual features from the third region of the overhead image; andcharacterize a second value of the metric of the tree based on the thirdset of visual features. The computer system can then compile values ofthe metric, for the first set of trees and the second set of trees, intoa composite value of the metric for the stand of trees. The computersystem can further present the composite value of the metric for thestand of trees within a user portal for review by a stand manager and/orowner of the stand of trees.

Therefore, the computer system can extract raw metrics corresponding tothe stand of trees collected by the aerial vehicle, extrapolateadditional metrics describing characteristics of the stand of trees andassign these additional metrics to individual trees. The computer systemcan then interpolate characteristics of other unscanned trees within thestand model to predict additional characteristics of the stand of treeswithin the stand model. Additionally, the computer system can assemblethe raw and derived metrics into a comprehensive data set representingthe stand of trees and annotate the three-dimensional representation ofthe stand of trees with the comprehensive data set to enable the owneror stand manager to execute management decisions regarding the stand oftrees such as harvest times, pest mitigation strategies, fire riskmitigation, etc.

14.1.1 Overhead Image+Confidence Scores

In one variation, the computer system can implement machine learning andregression techniques to represent the metric function as a linearregression between values of the metric and visual features of the standof trees. In this variation, the computer system can leverage the set ofcontainers and the linear regression to generate a confidence functionto calculate confidence scores of trees depicted in the low-resolutionoverhead image.

For example, the computer system can: extract an error value from thelinear regression representing the relationship between values of themetric and visual features of the stand of trees. The computer systemcan: derive a second correlation between the error value of the linearregression and visual features of the first set of trees; and generate aconfidence function representing the second correlation. Then, for eachtree in the second set of trees, depicted in a region of the overheadimage, the computer system can: estimate error of the value of themetric based on the confidence function and the visual features of thesecond set of trees; and represent error of the value as a confidencescore. The computer system can then generate a visual representation ofconfidence scores for the second set of trees and further annotate thecomposite value of the metric for the stand of trees with the virtualrepresentation of confidence scores for the second set of trees.

Furthermore, the computer system can: isolate a confidence score (e.g.,65%) of a particular tree—depicted in the low-resolution overheadimage—within the virtual representation of confidence scores; and, inresponse to the confidence score (e.g., 65%) falling below a thresholdconfidence score (e.g., 80%), generate a prompt for the aerial vehicleto capture high-resolution ground images of this particular tree and/ora set of trees proximal to this particular tree and represented in thelow-resolution overhead image. The computer system can access groundimages representing bases of this set of trees, isolate the set oftrees, and then implement methods and techniques described above torepresent errors of the value of the metric of each tree with an updatedconfidence score (e.g., 83%). The computer system can then: populate thevirtual representation with updated confidence scores for this set oftrees; annotate the composite value of the metric for the stand of treeswith the virtual representation of updated confidence scores; and renderthe composite value of the metric for the stand of trees forpresentation within the user portal.

Additionally or alternatively, the computer system can transform thevirtual representation of confidence scores into a tolerance intervalfor base diameters of trees depicted in the low-resolution overheadimage of the stand of trees. The computer system can then annotate thecomposite value of the base diameter for the stand of trees with thetolerance interval for base diameters.

Therefore, the computer system can leverage the linear regression toconstruct a confidence function to calculate confidence scores of trees.The computer system can monitor confidence scores of trees depicted inthe low-resolution overhead image and send the aerial vehicle to capturehigh-resolution ground images of a tree exhibiting a confidence scoreless than a threshold confidence score. The computer system can furthercalculate an updated confidence score of the tree proportional to theresolution of the ground image depicting the tree, as described below.

14.1.2 Ground Images+Confidence Scores

In another variation, the computer system can: assign confidence scoresto each tree depicted in the high-resolution set of ground imagesproportional to the resolution of the image depicting each tree; andfurther represent these confidence scores in the virtual representation.

For example, for each tree in the first set of trees, depicted in theset of ground images, the computer system can: calculate a confidencescore (e.g., 90%) of the value of metric of the tree proportional to aresolution (e.g., high-resolution) of the first region of the set ofground images depicting the tree; and store the confidence score, thevalue of the metric, and the second set of visual features in thecontainer, in the set of containers. The computer system can then:populate the virtual representation of confidence scores with confidencescores of the first set of trees; and annotate the composite value ofthe metric for the stand of trees with this updated virtualrepresentation of confidence scores.

14.2 Metric Variation: Base Diameter of Trees

In one variation, the computer system can detect visual featuresrepresenting a base boundary of a base of each tree depicted in ahigh-resolution ground image and leverage these base boundaries tocharacterize a base diameter of each tree. The computer system can thengenerate a metric function linking base diameters and base boundaries oftrees to characterize a base diameter of each tree depicted in thelow-resolution overhead image of the stand of trees.

For example, the computer system can: identify a ground image depictinga particular tree; detect a base boundary of a base of the particulartree in the ground image; and characterize a base diameter of theparticular tree based on the base boundary of the particular tree. Thecomputer system can then: detect a canopy of the particular tree in aregion of the overhead image; extract a particular set of visualfeatures from the particular region of the overhead image; store thebase diameter as a particular value of the metric in a containerassociated with the particular tree; and represent the particular set ofvisual features in the container.

Additionally or alternatively, the computer system can access a set ofhigh-resolution, two-dimensional color images representing bases of thestand of trees captured by the suite of optical sensors mounted theaerial vehicle. The computer system can then detect visual featureswithin each color image representing a pixel width of the base of eachtree and leverage these pixel widths to characterize a base diameter ofeach tree. The computer system can similarly generate a metric functionlinking base diameters and pixel widths of trees to characterize a basediameter of each tree depicted in the low-resolution overhead image ofthe stand of trees.

For example, the computer system can: access a set of color imagesrepresenting bases of the stand of trees and captured by the aerialvehicle proximal a floor of the stand of trees; identify a color image,in the set of color images, depicting a base of the particular tree;extract a pixel width of the base of the particular tree in the colorimage; and transform the pixel width into a base diameter of theparticular tree. The computer system can then: detect a canopy of theparticular tree in a region of the overhead image; extract a particularset of visual features from the region of the overhead image; store thebase diameter as a particular value of the metric in a containerassociated with the particular tree; and represent the particular set ofvisual features in the container.

Furthermore, the computer system can implement methods and techniquesdescribed above to estimate error of each base diameter metric andrepresent these errors as confidence values in a visual representationof confidence scores for the stand of trees. The computer system canfurther annotate the composite value of base diameters for the stand oftrees with the virtual representation of confidence scores and presentthe annotated composite value of base diameters for the stand of treeswithin the user portal for review by the user (e.g., stand manager,owner), as further described below.

14.3 Metric Variation: Tree Type+Foliage Characteristics

In one variation, the computer system can detect visual featuresrepresenting foliage characteristics of a particular tree from a groundimage captured by the aerial vehicle and leverage these foliagecharacteristics to identify the tree type and characterize a value ofthe tree type of the particular tree.

For example, the computer system can: identify an image, in the set ofground images, depicting a particular tree; extract a particular set offoliage characteristics (e.g., lance-shaped leaves) of the particulartree in the image; access a set of nominal foliage characteristicsassociated with a tree type (e.g., lance-shaped leaves associated with apine tree); and, in response to identifying the particular set offoliage characteristics analogous to the set of nominal foliagecharacteristics, characterize a particular value representing the treetype (e.g., pine tree) of the particular tree. The computer system canthen extract a secondary set of foliage characteristics (e.g.,needle-shaped leaves) from a particular region of the overhead image;calculate a particular confidence score of the particular valuerepresenting the tree type of the particular tree proportional to theresolution of the particular image; store the particular confidencescore of the particular value representing the tree type in a containerassociated with the particular tree; and represent the secondary set offoliage characteristics (e.g., needle-shaped leaves) in the container.The computer system can then generate a virtual representation ofconfidence scores representing tree types of trees depicted in the setof ground images; and compile values of the metric into the compositevalue for the stand of trees annotated with the virtual representationof confidence scores representing tree types for these trees.

Therefore, the computer system can leverage foliage characteristics fromhigh-resolution ground images and secondary foliage characteristics fromthe low-resolution overhead image to identify a tree type of each treein the stand of trees, to assign a confidence score to each tree type,and to generate a virtual representation of confidence scores for thestand of trees.

14.4 Metric Variation: Bark Characteristics

In one variation, the computer system can identify a particular treewithin a first scan zone within the three-dimensional representation ofthe stand of trees and extract the set of ground images captured by theaerial vehicle from the three-dimensional representation of the stand oftrees. The computer system can then detect a set of visual featuresrepresenting bark characteristics of a particular tree within the firstscan zone. The computer system can leverage the tree type of the tree toaccess a set of nominal (e.g., baseline) bark characteristics for theparticular tree. The computer system can then identify a differencebetween the visual features and the nominal set of bark characteristicsto characterize a pest risk value of the particular tree.

For example, the computer system can: identify an image, in the set ofground images, depicting a particular tree; extract a particular set ofbark characteristics of the particular tree from the image; access a setof nominal bark characteristics of the particular tree; characterize adifference between the particular set of bark characteristics and theset of nominal bark characteristics of the particular tree; andcharacterize a pest presence value of the particular tree based on thedifference between the particular set of bark characteristics and theset of nominal bark characteristics of the particular tree. The computersystem can then: detect a canopy of the particular tree in a region ofthe overhead image; extract a particular set of visual features from theregion of the overhead image; store the pest presence value as aparticular value of the metric in a container associated with theparticular tree; and represent the particular set of visual features inthe container. Then, based on the container, the computer system can:calculate a particular confidence score of the pest presence value ofthe particular tree proportional to a resolution of the image depictingthe particular tree; and, in response to the particular confidence scoreexceeding a threshold confidence score, generate a virtualrepresentation of pest presence value for the particular tree. Thecomputer system can then: compile the composite value of the metric forthe stand of trees, the virtual representation of the pest presencevalue for the particular tree, and the image, in the set of groundimages, into a report for the stand of trees; and render the report forthe stand of trees for presentation within a user portal.

The computer system can repeat the foregoing methods and techniques forother trees in the scan zone or trees in other scan zones, to predictpest risk values of these other trees. The computer system can furtheraggregate the pest risk values of individual trees within a scan zone togenerate a pest risk value of the scan zone, and/or aggregate pest riskvalues across the three-dimensional representation of the stand model topredict areas of pest risk. The computer system can also identify otherextrapolated metrics from the three-dimensional representation of thestand of trees such as fire risk, disease or blight, timber quality(e.g., rotten trees, damaged trees), carbon capture, etc.

15. Audit Tool

In one implementation, the computer system can present metrics selectedby the user from the aggregated scan data within the user portal. Thesemetrics can include: total tree count; tree count by species; volume oftimber; board feet of timber; pest presence/severity/location; healthytree percentage; and carbon capture potential. The computer system cansegment the three-dimensional representation of the stand of trees intoa set of layers describing a metric of the stand of trees such asharvest times and harvest locations for zones within the stand of trees,growth metrics and predictions, yield metrics and predictions, speciescount, tree damage and/or loss, pest presence, fire risk, and/or carboncapture and sequestration metrics.

Additionally, the computer system can present an image of arepresentative tree captured by the aerial vehicle during a scan of thestand of trees. The computer system can receive annotations from theuser representing bark conditions indicating pests, tree species, treedamage, or favorable growth characteristics displayed in the imagedepicting the representative tree. The computer system can then applythe received user input to the stand model to improve the accuracy ofpredictions or derived correlations completed by the predictive standmodel.

For example, at the conclusion of a flight path through the stand oftrees, a user accesses the user interface (e.g., user portal) andreviews metrics of the stand of trees. The computer system can present amap of the stand of trees denoting detected treetops, and a tableindicating metrics within a section of the stand of trees within theuser portal. The computer system also presents overhead images of thestand of trees and virtually overlays non-optical data to indicateindividual trees, scan zones, and other tree characteristics. In onevariation, the computer system can also present an annotated imagedepicting a representative tree within the scan zone. The user can theninspect the annotated image and verify the tree species and treecondition and validate the accuracy of the predictive stand model.

In another variation, the computer system can present a real-time imageof a representative tree captured by the aerial vehicle during executionof the flight path. The computer system can enrich the real-time imageby annotating the image with data from the predictive stand model andpresent the enriched representative image with annotations to the userwithin the user portal. The user can evaluate and verify thecorrelations derived by the predictive stand model within the userportal to improve the predictive stand model accuracy in real time.

Therefore, the computer system can present the annotated predictivestand model to a user in a user-readable format within the user portaland receive input from the user to verify the accuracy of modelpredictions executed by the computer system to characterize the stand oftrees. The computer system can then present data representing the standof trees to a user (e.g., an owner or manager of the stand of trees),receive input from the user to verify the model accuracy, and generateand transmit prompts to the user to select a stand management action,and/or produce a management report describing current and projectedstates of the stand of trees based on the predictive stand model.

15.1 Confidence Score Filter

In one variation, the user may want to review a particular metric oftrees characterized by a confidence score within a confidence scorerange within the user portal. The user can interface with the userportal to define a confidence score filter for the values of the metric.

For example, the computer system can render the composite value for thestand of trees for presentation within a user portal and, in response toreceiving selection of a confidence score range (e.g., between 75% and85%) for a set of trees—depicted in the low-resolution overhead image—atthe user portal from a user: isolate confidence scores of this set oftrees falling within the confidence score range (e.g., between 75% and85%); and generate a second virtual representation of these confidencescores for the set of trees. The computer system can then: annotate thecomposite value of the metric for the stand of trees with the secondvirtual representation of confidence scores for this set of trees; andpresent the composite value of the metric for the stand of trees,annotated with the second virtual representation of confidence scoresfor the set of trees, within the user portal for the user to review.

Therefore, the user can interface with the user portal and the computersystem can apply a confidence score filter to trees exhibiting a metricof interest to the user to enable the user to review trees within aconfidence score range.

15.2 Management Report

In one variation, the computer system can present a management report ofthe stand of trees to the user within the user portal. The managementreport can include the composite value of a metric of interest to theuser, a virtual representation of confidence scores, and high-resolutionground images representing bases of trees associated with the metric.

For example, the user may select a base diameter metric within the userportal. The computer system can then implement methods and techniquesdescribed above to calculate a confidence score of a base diameter of aparticular tree proportional to a resolution of the ground imagedepicting the tree; generate a virtual representation of the confidencescore for the particular tree; compile the composite value of the metricfor the stand of trees, the virtual representation of this confidencescore for the particular tree, and the ground image depicting theparticular tree, into a management report for the stand of trees; andpresent this management report within the user portal to the user.

Furthermore, the user may select a base diameter heatmap for the standof trees within the user portal. The computer system can then: extract avalue of the metric (e.g., base diameter) of the particular tree from acontainer associated with the particular tree and, in response to thebase diameter falling within a target base diameter range, assign afirst color value to the base diameter of the particular tree. Thecomputer system can repeat this process for each other base diameter ofeach other tree. The computer system can then generate a heatmap of basediameters for the stand of trees populated with the color valuerepresenting the base diameter of the particular tree; and render theheatmap of base diameters for the stand of trees within the user portal.

Additionally or alternatively, the user may select a histogram of treequantities for the stand of trees within the user portal. The computersystem can then: isolate a first set of trees depicted in the set ofground images; detect a first tree quantity of the first set of trees;isolate a second set of trees depicted in the overhead image; detect asecond tree quantity of the second set of trees; and calculate a totaltree quantity of the stand of trees based on a combination of the firsttree quantity and the second tree quantity. The computer system can thenimplement methods and techniques described above to identify a firstsubset of trees exhibiting base diameters within a first discrete basediameter range (e.g., between 12 inches and 14 inches); and identify asecond subset of trees exhibiting base diameters within a seconddiscrete base diameter range (e.g., between 14 inches and 19 inches);and aggregate the total tree quantity and base diameters of the firstsubset of trees and the second subset of trees into a histogram of treequantities for the stand of trees.

Therefore, the computer system can present metrics of the stand of treesas a management report, as a heatmap of base diameters, and/or as ahistogram of tree quantities for review by the user within the userportal.

The systems and methods described herein can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a user computer or mobile device,wristband, smartphone, or any suitable combination thereof. Othersystems and methods of the embodiment can be embodied and/or implementedat least in part as a machine configured to receive a computer-readablemedium storing computer-readable instructions. The instructions can beexecuted by computer-executable components integrated bycomputer-executable components integrated with apparatuses and networksof the type described above. The computer-readable medium can be storedon any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, or any suitable device. The computer-executable component can bea processor but any suitable dedicated hardware device can(alternatively or additionally) execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the invention without departing fromthe scope of this invention as defined in the following claims.

I claim:
 1. A method comprising: accessing a boundary of a stand oftrees; defining an array of scan zones within the boundary of the standof trees; defining a flight path comprising: a first waypoint at a firstaltitude above a first set of trees within a first scan zone; a secondwaypoint at a second altitude proximal a first floor within the firstscan zone; a third waypoint at a third altitude proximal a second floorwithin a second scan zone; a fourth waypoint at a fourth altitude abovea second set of trees within the second scan zone; and a fifth waypointat a fifth altitude above a third set of trees within a third scan zone;accessing a first set of images comprising: a first sequence of imagesrepresenting tops of the first set of trees and captured by an aerialvehicle proximal the first waypoint; a second sequence of imagesrepresenting bases of the first set of trees and captured by the aerialvehicle proximal the second waypoint; a third sequence of imagesrepresenting bases of the second set of trees and captured by the aerialvehicle proximal the third waypoint; a fourth sequence of imagesrepresenting tops of the second set of trees and captured by the aerialvehicle proximal the fourth waypoint; and a fifth sequence of imagesrepresenting tops of the third set of trees and captured by the aerialvehicle proximal the fifth waypoint; interpolating a first set of treecanopy characteristics of a fourth set of trees between the first scanzone and the second scan zone based on visual features detected in thefirst sequence of images and the fourth sequence of images;interpolating a first set of lower tree characteristics of the fourthset of trees between the first scan zone and the second scan zone basedon visual features detected in the second sequence of images and thethird sequence of images; interpolating a second set of tree canopycharacteristics of a fifth set of trees between the second scan zone andthe third scan zone based on visual features detected in the fourthsequence of images and the fifth sequence of images; and compiling thefirst set of tree canopy characteristics, the first set of lower treecharacteristics, and the second set of tree canopy characteristics intoa virtual representation of tree characteristics across the stand oftrees.
 2. The method of claim 1: further comprising: deploying theaerial vehicle to capture images while traversing the flight path;accessing a first intermediate vertical sequence of images representingtrunks of the first set of trees and captured by the aerial vehiclewhile navigating from the first waypoint to the second waypoint;accessing a second intermediate horizontal sequence of images capturedby the aerial vehicle while navigating from the second waypoint to thethird waypoint; accessing a third intermediate vertical sequence ofimages representing trunks of the second set of trees captured by theaerial vehicle while navigating from the third waypoint to the fourthwaypoint; accessing a fourth intermediate horizontal sequence of imagescaptured by the aerial vehicle while navigating from the fourth waypointto the fifth waypoint; and interpolating a first set of tree trunkcharacteristics of the fourth set of trees between the first scan zoneand the second scan zone based on visual features detected in the firstintermediate vertical sequence of images and the third intermediatevertical sequence of images; and wherein compiling the first set of treecanopy characteristics comprises compiling the first set of tree canopycharacteristics, the first set of lower tree characteristics, the firstset of tree trunk characteristics, and the second set of tree canopycharacteristics into the virtual representation of tree characteristicsacross the stand of trees.
 3. The method of claim 1: further comprising:accessing an overhead image depicting the stand of trees; and overlayingthe boundary of the stand of trees onto the overhead image; and whereindefining the array of scan zones within the boundary of the stand oftrees comprises projecting a two-dimensional grid array of scan zonesonto the overhead image, each scan zone in the array of scan zones:defining a minimum diameter within a target diameter range; defining alateral pitch distance: greater than the minimum diameter; and less thana maximum width of the boundary of the stand of trees; and defining alongitudinal pitch distance: greater than the minimum diameter; and lessthan a maximum length of the boundary of the stand of trees.
 4. Themethod of claim 3: wherein accessing the overhead image depicting thestand of trees comprises: deploying the aerial vehicle to capture imagesof the stand of trees at a sixth altitude greater than the firstaltitude above the stand of trees; accessing an initial sequence ofimages representing tops of the stand of trees, the initial sequence ofimages characterized by an initial resolution, and captured by theaerial vehicle; and compiling the initial sequence of images into acomposite overhead image of the stand of trees characterized by theinitial resolution; and wherein accessing the first set of imagescomprises accessing the first set of images characterized by a firstresolution greater than the initial resolution.
 5. The method of claim1: further comprising: extracting a first set of visual features fromthe first sequence of images; deriving a first set of heights of thefirst set of trees, based on the first set of visual features;extracting a second set of visual features from the second sequence ofimages; deriving a first set of base diameters of the first set of treesbased on the second set of visual features; and calculating atree-base-to-height ratio based on a first combination of the first setof heights and a second combination of the first set of diameters;wherein interpolating a first set of tree canopy characteristics of thefourth set of trees between the first scan zone and the second scan zonecomprises: applying the tree-base-to-height ratio to the first set ofcanopy characteristics of the fourth set of trees; and in response tothe first set of canopy characteristics corresponding to thetree-base-to-height ratio, predicting a second set of heights of thefourth set of trees; and wherein interpolating a first set of lower treecharacteristics of the fourth set of trees between the first scan zoneand the second scan zone comprises: applying the tree-base-to-heightratio to the first set of lower tree characteristics of the fourth setof trees; and in response to the first set of lower tree characteristicscorresponding to the tree-base-to-height ratio, predicting a second setof base diameters of the fourth set of trees.
 6. The method of claim 1:further comprising: defining a first orientation for a first raster legacross the first scan zone; defining a second orientation orthogonal tothe first orientation for a second raster leg across the first scanzone; defining a third orientation orthogonal to the second orientationand opposite the first raster leg for a third raster leg across thefirst scan zone; and aggregating the first raster leg, the second rasterleg, and the third raster leg into a boustrophedonic strip cruise forexecution by the aerial vehicle; and wherein accessing the first set ofimages comprises accessing the first set of images captured by theaerial vehicle while navigating the boustrophedonic strip cruise.
 7. Themethod of claim 1: further comprising: extracting a third set of treecanopy characteristics of the first set of trees from the first sequenceof images; extracting a second set of lower tree characteristics of thefirst set of trees from the second sequence of images; generating afirst tree model of the first scan zone linking the third set of treecanopy characteristics and the second set of lower tree characteristicsof the first set of trees; extracting a third set of lower treecharacteristics of the second set of trees from the third sequence ofimages; extracting a fourth set of tree canopy characteristics of thesecond set of trees from the fourth sequence of images; generating asecond tree model of the second scan zone linking the third set of lowertree characteristics and the fourth set of tree canopy characteristicsof the second set of trees; accessing an overhead image depicting thestand of trees; and interpolating a set of total tree characteristics ofthe stand of trees based on visual features detected in the overheadimage, the first tree model of the first scan zone, and the second treemodel of the second scan zone; and wherein compiling the first set oftree canopy characteristics, the first set of lower treecharacteristics, and the second set of tree canopy characteristics intothe virtual representation comprises compiling the set of total treecharacteristics into the virtual representation of tree characteristicsacross the stand of trees.
 8. The method of claim 1: further comprising:accessing an overhead image depicting the stand of trees; extracting afirst set of visual features from a first region of the overhead imagerepresenting tree characteristics of the first set of trees; extractinga second set of visual features from a second region above the firstregion of the overhead image representing tree characteristics of thesecond set of trees; and characterizing a first difference between thefirst set of visual features and the second set of visual features; andwherein defining the array of scan zones within the boundary of thestand of trees comprises in response to the first difference between thefirst set of features and the second set of features exceeding adifference threshold: projecting the first scan zone, in the array ofscan zones, onto the first region of the overhead image to encompass thefirst set of trees; and projecting the second scan zone, in the array ofscan zones, onto the second region of the overhead image to encompassthe second set of trees.
 9. The method of claim 1: further comprising:accessing a first intermediate vertical sequence of images representingtrunks of the first set of trees and captured by the aerial vehiclewhile navigating along the flight path from the first waypoint to thesecond waypoint; assembling the first sequence of images, the secondsequence of images, and the first intermediate vertical sequence ofimages into a first three-dimensional representation of the first scanzone; accessing a second intermediate vertical sequence of imagesrepresenting trunks of the second set of trees captured by the aerialvehicle while navigating along the flight path from the third waypointto the fourth waypoint; and assembling the third sequence of images, thefourth sequence of images, and the second intermediate vertical sequenceof images into a second three-dimensional representation of the secondscan zone; and wherein compiling the first set of tree canopycharacteristics, the first set of lower tree characteristics, and thesecond set of tree canopy characteristics into the virtualrepresentation comprises assembling the first three-dimensionalrepresentation of the first scan zone and the second three-dimensionalrepresentation of the second scan zone into a third three-dimensionalrepresentation of the stand of trees.
 10. The method of claim 9, furthercomprising: extracting a third set of tree canopy characteristics of thefirst set of trees from the third three-dimensional representation ofthe stand of trees; accessing a set of target tree canopycharacteristics associated with a tree type; in response to the thirdset of tree canopy characteristics corresponding to the set of targettree canopy characteristics, annotating the first set of trees with thetree type within the third three-dimensional representation of the standof trees; and interpolating a tree type of the stand of trees based onthe third three-dimensional representation of the stand of trees. 11.The method of claim 9: further comprising, accessing a first set ofdepth images comprising: a first sequence of depth images representingtops of the first set of trees and captured by the aerial vehicleproximal the first waypoint; a second sequence of depth imagesrepresenting bases of the first set of trees and captured by the aerialvehicle proximal the second waypoint; and a first intermediate verticalsequence of depth images representing trunks of the first set of treesand captured by the aerial vehicle while navigating along the flightpath from the first waypoint to the second waypoint; and whereinassembling the first three-dimensional representation of the first scanzone comprises compiling the first sequence of images, the secondsequence of images, the first intermediate vertical sequence of images,the first sequence of depth images, second sequence of depth images, andthe first intermediate vertical sequence of depth images into a colorthree-dimensional representation of the first scan zone.
 12. The methodof claim 1, further comprising: extracting a third set of tree canopycharacteristics of the first set of trees from the first sequence ofimages; deriving a first set of tree base diameters based on visualfeatures detected in the second sequence of images; generating a treecharacteristics model linking the third set of tree canopycharacteristics to the first set of tree base diameters; accessing anoverhead image depicting the stand of trees; isolating a region of theoverhead image, the region depicting a sixth set of trees excluded fromthe array of scan zones; extracting a set of visual features of thesixth set of trees from the overhead image; estimating a second set oftree base diameters of the sixth set of trees based on the set of visualfeatures and the tree characteristics model; and rendering the first setof tree base diameters of the first set of trees and the second set oftree base diameters of the sixth set of trees, excluded from the arrayof scan zones, for presentation within a user portal.
 13. The method ofclaim 12: wherein generating the tree characteristics model compriseslinking the third set of tree canopy characteristics and the virtualrepresentation of tree characteristics across the stand of trees;further comprising interpolating a fourth set of tree canopycharacteristics of the sixth set of trees based on the treecharacteristics model and the set of visual features; and whereincompiling the first set of tree canopy characteristics, the first set oflower tree characteristics, and the second set of tree canopycharacteristics into the virtual representation comprises compiling thefirst set of tree canopy characteristics, the first set of lower treecharacteristics, the second set of tree canopy characteristics, thethird set of tree canopy characteristics, and the fourth set of treecanopy characteristics into the virtual representation of treecharacteristics across the stand of trees.
 14. The method of claim 12:further comprising: deploying the aerial vehicle to capture images ofthe stand of trees at a sixth altitude greater than the first altitudeabove the stand of trees; accessing an initial sequence of imagesrepresenting tops of the stand of trees and captured by the aerialvehicle; and compiling the initial sequence of images into a compositeoverhead image of the stand of trees; and wherein accessing the overheadimage depicting the stand of trees comprises accessing the compositeoverhead image of the stand of trees.
 15. The method of claim 1: furthercomprising: accessing an overhead image depicting the stand of trees;isolating a region of the overhead image depicting the second scan zone;extracting a set of visual features from the region of the overheadimage; detecting a set of objects in the second scan zone based on theset of visual features; and in response to detecting the set of objectswithin a threshold distance of the second waypoint of the flight path,defining a sixth waypoint between the second waypoint and the thirdwaypoint of the flight path; and wherein accessing the set of imagescomprises accessing the set of images comprising the third sequence ofimages representing bases of the second set of trees and captured by theaerial vehicle proximal the sixth waypoint.
 16. A method comprising:accessing an overhead image depicting a stand of trees; overlaying aboundary of the stand of trees onto the overhead image; projecting anarray of scan zones onto the overhead image within the boundary of thestand of trees; deploying an aerial vehicle to execute a flight paththrough the array of scan zones; during the flight path: traversing theaerial vehicle across the stand of trees to a first waypoint above afirst scan zone; vertically traversing the aerial vehicle to a secondwaypoint proximal to a first floor of the first scan zone; laterallytraversing the aerial vehicle from the first scan zone to a thirdwaypoint proximal to a second floor of a second scan zone; andvertically traversing the aerial vehicle to a fourth waypoint above thesecond scan zone; accessing a set of images captured by the aerialvehicle comprising: a first sequence of images representing tops of afirst set of trees within the first scan zone; a first intermediatevertical sequence of images representing trunks of the first set oftrees; a second sequence of images representing bases of the first setof trees within the first scan zone; a third sequence of imagesrepresenting bases of a second set of trees within the second scan zone;a second intermediate vertical sequence of images representing trunks ofthe second set of trees; and a fourth sequence of images representingtops of the second set of trees within the second scan zone; compilingthe first sequence of images, the first intermediate vertical sequenceof images, and the second sequence of images into a firstthree-dimensional representation of the first scan zone; compiling thethird sequence of images, the second intermediate vertical sequence ofimages, and the fourth sequence of images into a secondthree-dimensional representation of the second scan zone; and assemblingthe first three-dimensional representation of the first scan zone andthe second three-dimensional representation of the second scan zone intoa third three-dimensional representation of the stand of trees.
 17. Themethod of claim 16: further comprising: interpolating a first set oftree canopy characteristics of a third set of trees between the firstscan zone and the second scan zone based on visual features detected inthe first sequence of images and the fourth sequence of images; andinterpolating a first set of lower tree characteristics of the third setof trees between the first scan zone and the second scan zone based onvisual features detected in the second sequence of images and the thirdsequence of images; and wherein assembling the third three-dimensionalrepresentation of the stand of trees comprises compiling the first setof tree canopy characteristics and the first set of lower treecharacteristics of the third set of trees into a virtual representationof tree characteristics across the stand of trees.
 18. The method ofclaim 17, further comprising: further comprising: extracting a secondset of tree canopy characteristics of the first set of trees from thefirst sequence of images; extracting a third set of tree canopycharacteristics from the fourth sequence of images; generating a treecharacteristics model linking the first set of tree canopycharacteristics, the second set of tree canopy characteristics, thethird set of tree canopy characteristics, and the thirdthree-dimensional representation of the stand of trees; isolating afirst region of the overhead image representing a fourth set of treesexcluded from the first scan zone and the second scan zone; extracting aset of tree characteristics of the fourth set of trees from the firstregion of the overhead image; and interpolating a fourth set of treecanopy characteristics of the fourth set of trees based on the treecharacteristics model and the set of tree characteristics; and whereinassembling the third three-dimensional representation of the stand oftrees comprises compiling the first set of tree canopy characteristics,the second set of tree canopy characteristics, the third set of treecanopy characteristics, and the fourth set of tree canopycharacteristics into the virtual representation of tree characteristicsacross the stand of trees.
 19. A method comprising: accessing a boundaryof a stand of trees; defining an array of scan zones within the boundaryof the stand of trees; defining a flight path comprising a set ofwaypoints representing positions within the array of scan zones;accessing a set of images captured by the aerial vehicle comprising: afirst sequence of images representing tops of a first set of treeswithin a first scan zone; a second sequence of images representing basesof the first set of trees within the first scan zone; a third sequenceof images representing bases of a second set of trees within a secondscan zone; a fourth sequence of images representing tops of the secondset of trees; and a fifth sequence of images representing tops of athird set of trees within a third scan zone; interpolating a first setof tree canopy characteristics of a fourth set of trees between thefirst scan zone and the second scan zone based on visual featuresdetected in the first sequence of images and the fourth sequence ofimages; interpolating a first set of lower tree characteristics of thefourth set of trees between the first scan zone and the second scan zonebased on visual features detected in the second sequence of images andthe third sequence of images; interpolating a second set of tree canopycharacteristics of a fifth set of trees between the second scan zone andthe third scan zone based on visual features detected in the fourthsequence of images and the fifth sequence of images; and compiling thefirst set of tree canopy characteristics, the first set of lower treecharacteristics, and the second set of tree canopy characteristics intoa virtual representation of tree characteristics across the stand oftrees.
 20. The method of claim 19, wherein defining the flight pathcomprising the set of waypoints representing positions within the arrayof scan zones comprises defining the flight path comprising: a firstwaypoint at a first altitude above the first set of trees within thefirst scan zone; a second waypoint at a second altitude proximal a firstfloor within the first scan zone; a third waypoint at the secondaltitude above a second floor within the second scan zone; a fourthwaypoint at the first altitude above the second set of trees within thesecond scan zone; and a fifth waypoint at the first altitude above thethird set of trees within the third scan zone.